The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. H...The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.展开更多
This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated deman...This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly proposed.Specifically,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be reduced.The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units.The heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park.The proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming formulation.The alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high quality.Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.展开更多
This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involv...This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.展开更多
Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are u...Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.展开更多
The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefor...The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.展开更多
The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbani...The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.展开更多
Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy ...Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy saving initiatives, municipalities play a strategic role in the energy planning process. This paper focuses on establishment of an energy planning methodology for small municipalities with numbers of inhabitants in range of 1,000-10,000 which often face common problems associated with low efficient district heat supply systems and decreasing energy consumption in buildings. Particular attention is paid to DSM （demand side management） activities. DSM scheme includes legislative and financial flows with small investments from municipality side. Based on increased information and motivation it promotes reduction of energy consumption in all kinds of buildings. Practical experience has shown that application of DSM measures allows achieving 20% energy savings in municipal buildings during the first year.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes ...In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.展开更多
The main aim of this study is to assess various aspects of the current water policy, investigate the perspectives of water saving, evaluate water price elasticity and explore new approaches toward sustainable water ma...The main aim of this study is to assess various aspects of the current water policy, investigate the perspectives of water saving, evaluate water price elasticity and explore new approaches toward sustainable water management in the water sector, through a questionnaire survey that has been performed in the city of Volos, Greece, concerning the residential sector. The appropriate design of water management measures presupposes the investigation of the influence of some selected variables to consumers＇ behavior. The price of water, the size of the dwelling, the indoor and outdoor uses, the educational level, the income of consumers as well as rainfall and temperature levels are examined, the residential water demand curve is estimated and projections of future water demand under different pricing policies are performed.展开更多
Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufactur...Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.展开更多
Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and beh...Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.展开更多
In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on th...In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.展开更多
Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems.Conventionally,DRL agents are trained by randomly selecting samp...Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems.Conventionally,DRL agents are trained by randomly selecting samples from a data set,which can result in overexposure to some data categories and under/no exposure to other data categories.Thus,the trained model may be biased towards some data groups and underperform(provide suboptimal results)for data groups to which it was less exposed.To address this issue,diversity in experience-based DRL agent training framework is proposed in this study.This approach ensures the exposure of agents to all types of data.The proposed framework is implemented in two steps.In the first step,raw data are grouped into different clusters using the K-means clustering method.The clustered data is then arranged by stacking the data of one cluster on top of another.In the second step,a selection algorithm is proposed to select data from each cluster to train the DRL agent.The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method.To analyze the performance of the proposed approach and compare the results with the conventional random selection method,two indices are proposed in this study:the flatness index and the divergence index.The model is trained using different data sets(1-year,3-year,and 5-year)and also with the inclusion of solar photovoltaics.The simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.展开更多
Like many river basins in China, water resources in the Fudong Pai River are almost fully allocated. This paper seeks to assess and evaluate water resource problems using water evaluation and planning (WEAP) model via...Like many river basins in China, water resources in the Fudong Pai River are almost fully allocated. This paper seeks to assess and evaluate water resource problems using water evaluation and planning (WEAP) model via its application to Hengshui Basin of Fudong Pai River. This model allows the simulation and analysis of various water allocation scenarios and, above all, scenarios of users' behavior. Water demand management is one of the options discussed in detail. Simulations are proposed for diverse climatic situations from dry years to normal years and results are discussed. Within the limits of data availability, it appears that most water users are not able to meet all their requirements from the river, and that even the ecological reserve will not be fully met during certain years. But the adoption of water demand management procedures offers opportunities for remedying this situation during normal hydrological years. However, it appears that demand management alone will not suffice during dry years. Nevertheless, the ease of use of the model and its user-friendly interfaces make it particularly useful for discussions and dialogue on water resources management among stakeholders.展开更多
As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission cont...As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.展开更多
Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,redu...Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.展开更多
The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the po...The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the potential roll-out of home energy management systems(HEMSs),which provide a means for prosumers to respond to externalities such as energy price,weather,and energy demands.However,the economic operation of prosumers can affect grid security,especially when energy prices are extremely low or high.Therefore,it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely,the network operator,prosumer,and aggregator.In this paper,a novel transactive energy(TE)-based operational framework is proposed.Under this frame-work,aggregators interact with the distribution grid operator through a negotiation process to ensure network security,while at the lower level,prosumers submit their schedule to the aggregator through the HEMS.If network security is at risk,aggregators will send an additional price component representing the cost of security(CoS)to the prosumer to stimulate further response.The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.展开更多
China has started shifting from relying on supply management to demand management strategy in addressing its water shortage problems.Water option,a financial derivative for water commodity,has been utilized to manage ...China has started shifting from relying on supply management to demand management strategy in addressing its water shortage problems.Water option,a financial derivative for water commodity,has been utilized to manage water demands in the United States and Europe since the 1990 s but is still novel to China.In this study we analyzed the pros and cons of China’s existing system for water rights transfers and proposed an alternative,flexible trading instrument-water options for China.Incorporating the uncertainty to water option pricing,this study first conducted an empirical analysis of the water option in the water-receiving area of the Hanjiang-Weihe River Transfer Project of China,and then evaluated the benefits of the water option applications.Results show that water option trading can bring water cost saving and increase the potential industrially added value for industrial enterprises in the receiving area,and trading of short-and-medium term water options is more favorable than the long-term water options trading.The novel water option trading proposed in this study,once verified through pilot studies,will be helpful in addressing water shortage problems in China.展开更多
基金supported in part by the National Science Foundation of China (61973247, 61673315, 62173268)the Key Research and Development Program of Shaanxi (2022GY-033)+2 种基金the Nationa Postdoctoral Innovative Talents Support Program of China (BX20200272)the Key Program of the National Natural Science Foundation of China (61833015)the Fundamental Research Funds for the Central Universities (xzy022021050)。
文摘The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.
基金National Basic Research Program of China(2010CB428406)Natural Science Foundation of China(No.40671035)the Knowledge Innovation Key Project of Chinese Academy of Sciences(Kzcx2-yw-126)
基金This work was supported by the National Key R&D Program of China(No.2018YFB0905000)the Science and Technology Project of State Grid Corporation of China(No.SGTJDK00DWJS1800232).
文摘This paper proposes a decentralized demand management approach to reduce the energy bill of industrial park and improve its economic gains.A demand management model for industrial park considering the integrated demand response of combined heat and power(CHP)units and thermal storage is firstly proposed.Specifically,by increasing the electricity outputs of CHP units during peak-load periods,not only the peak demand charge but also the energy charge can be reduced.The thermal storage can efficiently utilize the waste heat provided by CHP units and further increase the flexibility of CHP units.The heat dissipation of thermal storage,thermal delay effect,and heat losses of heat pipelines are considered for ensuring reliable solutions to the industrial park.The proposed model is formulated as a multi-period alternating current(AC)optimal power flow problem via the second-order conic programming formulation.The alternating direction method of multipliers(ADMM)algorithm is used to compute the proposed demand management model in a distributed manner,which can protect private data of all participants while achieving solutions with high quality.Numerical case studies validate the effectiveness of the proposed demand management approach in reducing peak demand charge,and the performance of the ADMM-based decentralized computation algorithm in deriving the same optimal results of demand management as the centralized approach is also validated.
基金This work was supported in part by the National Natural Science Foundation of China(61922076,61725304,61873252,61991403,61991400)in part by the Australian Research Council Discovery Program(DP200101199).
文摘This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.
文摘Microgrids are a type of restricted power distribution systems in which electricity is generated,transmitted,and distributed within a small geographic region.They are used to ensure that renewable energy sources are used to their full potential.Microgrids provide further benefits,such as lowering transmission losses and the expenses associated with them.This research compares and contrasts the aims of economic dispatch,emission dispatch,fractional programing based combined economic emission dispatch,and environmental restricted economic dispatch(ECED).A low-voltage microgrid system is investigated for three different scenarios.As a study optimization tool,an innovative,resilient,and strong hybrid swarm-intelligence optimization algorithm is utilised,which is based on combining the properties of the traditional grey-wolf optimiser,sine-cosine algorithm,and crow search algorithm.The employment of a time-of-use energy mar-ket pricing approach instead of a fixed pricing plan resulted in a 15%decrease in gen-eration costs throughout the course of the research.When ECED was assessed with a 15%-20%demand side management based restructured load demand model for the microgrid system,the generation costs were reduced even further.
基金supported by the Ministry of Science,Research and Arts(MWK)of Baden-Württemberg,Germany,as part of a Ph.D.scholarship
文摘The building sector and its heating and cooling represents one of the major consumer of energy worldwide. Simultaneously, the share of fluctuating generation of renewable energies in the energy mix increases. Therefore storage and demand side management technologies are required. The new adaptive and predictive control algorithm for thermally activated building systems （TABS） based on multiple linear regression （AMLR） presented in this paper enables the application of demand side management （DSM） strategies. Based on simulations, different strategies have been compared with each other. By applying the AMLR algorithm, electricity energy cost savings of 38% could be achieved compared to the conventional control strategy for TABS, while increasing the thermal comfort. At the same time, thermal energy demand can be reduced in the range between 4% to 8%, and pump operation time from 86% to 89%.
文摘The introduction of new kinds of energy mixes to the electricity grid is a challenging environmental task for present and future generations as they fight the pollution and global warming issues associated with urbanization. Individual appliances and whole buildings that continuously incorporate local intelligence which originates from the new technologies of Internet of Things are the new infrastructure that this integration is based on. Smart Electricity Grids are becoming more intensively integrated with tertiary building energy management systems and distributed energy generators such as wind and solar. This new smart network type harnesses the loT （lnternet of Things） principles by generating a new network made of active elements combined with the necessary control and distributed coordination mechanisms. This new self-organized overlay network of connected DER （distributed energy resources） allows for the seamless management and control of the active grid as well as the efficient coordination and exploration of single and aggregated technical prosumer potential （generation and consumption） to participate in energy balancing and other distributed grid related services, applying energy management strategies based on control and predict of the DERs behavior for facing demand side management issues.
文摘Recent estimates state that the European Union is on course to achieve only half of the 20% energy consumption reduction target by 2020. As the first governmental stakeholders involved in the implementation of energy saving initiatives, municipalities play a strategic role in the energy planning process. This paper focuses on establishment of an energy planning methodology for small municipalities with numbers of inhabitants in range of 1,000-10,000 which often face common problems associated with low efficient district heat supply systems and decreasing energy consumption in buildings. Particular attention is paid to DSM （demand side management） activities. DSM scheme includes legislative and financial flows with small investments from municipality side. Based on increased information and motivation it promotes reduction of energy consumption in all kinds of buildings. Practical experience has shown that application of DSM measures allows achieving 20% energy savings in municipal buildings during the first year.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
基金supported by the National Science Foundation(NSF)grant ECCF 1936494.
文摘In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.
文摘The main aim of this study is to assess various aspects of the current water policy, investigate the perspectives of water saving, evaluate water price elasticity and explore new approaches toward sustainable water management in the water sector, through a questionnaire survey that has been performed in the city of Volos, Greece, concerning the residential sector. The appropriate design of water management measures presupposes the investigation of the influence of some selected variables to consumers＇ behavior. The price of water, the size of the dwelling, the indoor and outdoor uses, the educational level, the income of consumers as well as rainfall and temperature levels are examined, the residential water demand curve is estimated and projections of future water demand under different pricing policies are performed.
基金Supported by National Natural Science Foundation of China(Grant No.61272428)PhD Programs Foundation of Ministry of Education of China(Grant No.20120002110067)
文摘Cyber physical systems（CPS） recently emerge as a new technology which can provide promising approaches to demand side management（DSM）, an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufac^ring center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.
文摘Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.
文摘In recent years,there has been a significant surge in demand for electric vehicles(EVs),necessitating accurate prediction of EV charging requirements.This prediction plays a crucial role in evaluating its impact on the power grid,encompassing power management and peak demand management.In this paper,a novel deep neural network based onα^(2)-LSTM is proposed to predict the demand for charging from electric vehicles at a 15-minute time resolution.Additionally,we employ AES-128 for station quantization and secure communication with users.Our proposed algorithm achieves a 9.2%reduction in both the Root Mean Square Error(RMSE)and the mean absolute error compared to LSTM,along with a 13.01%increase in demand accuracy.We present a 12-month prediction of EV charging demand at charging stations,accompanied by an effective comparative analysis of Mean Absolute Percentage Error(MAPE)and Mean Percentage Error(MPE)over the last five years using our proposed model.The prediction analysis has been conducted using Python programming.
基金supported by the Natural Sciences and Engineering Research Council(NSERC)of Canada,grant number RGPIN-2017-05866.
文摘Deep reinforcement learning(DRL)is a suitable approach to handle uncertainty in managing the energy consumption of buildings with energy storage systems.Conventionally,DRL agents are trained by randomly selecting samples from a data set,which can result in overexposure to some data categories and under/no exposure to other data categories.Thus,the trained model may be biased towards some data groups and underperform(provide suboptimal results)for data groups to which it was less exposed.To address this issue,diversity in experience-based DRL agent training framework is proposed in this study.This approach ensures the exposure of agents to all types of data.The proposed framework is implemented in two steps.In the first step,raw data are grouped into different clusters using the K-means clustering method.The clustered data is then arranged by stacking the data of one cluster on top of another.In the second step,a selection algorithm is proposed to select data from each cluster to train the DRL agent.The frequency of selection from each cluster is in proportion to the number of data points in that cluster and therefore named the proportional selection method.To analyze the performance of the proposed approach and compare the results with the conventional random selection method,two indices are proposed in this study:the flatness index and the divergence index.The model is trained using different data sets(1-year,3-year,and 5-year)and also with the inclusion of solar photovoltaics.The simulation results confirmed the superior performance of the proposed approach to flatten the building’s load curve by optimally operating the energy storage system.
文摘Like many river basins in China, water resources in the Fudong Pai River are almost fully allocated. This paper seeks to assess and evaluate water resource problems using water evaluation and planning (WEAP) model via its application to Hengshui Basin of Fudong Pai River. This model allows the simulation and analysis of various water allocation scenarios and, above all, scenarios of users' behavior. Water demand management is one of the options discussed in detail. Simulations are proposed for diverse climatic situations from dry years to normal years and results are discussed. Within the limits of data availability, it appears that most water users are not able to meet all their requirements from the river, and that even the ecological reserve will not be fully met during certain years. But the adoption of water demand management procedures offers opportunities for remedying this situation during normal hydrological years. However, it appears that demand management alone will not suffice during dry years. Nevertheless, the ease of use of the model and its user-friendly interfaces make it particularly useful for discussions and dialogue on water resources management among stakeholders.
文摘As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.
基金conducted at University of Engineering and Technology Lahore with support of Department of Transportation Engineering and Management Department
文摘Traffic congestion has become a critical issue in developing countries,as it tends to increase social costs in terms of travel cost and time,energy consumption and environmental degradation.With limited resources,reducing travel demand by influencing individuals’ travel behavior can be a better long-term solution.To achieve this objective,alternate travel options need to be provided so that people can commute comfortably and economically.This study aims to identify key motives and constraints in the consideration of carpooling policy with the help of stated preference questionnaire survey that was conducted in Lahore City.The designed questionnaire includes respondents’ socioeconomic demographics,and intentions and stated preferences on carpooling policy.Factor analysis was conducted on travelers’ responses,and a structural model was developed for carpooling.Survey and modeling results reveal that social,environmental and economic benefits,disincentives on car use,preferential parking treatment for carpooling,and comfort and convenience attributes are significant determinants in promoting carpooling.However,people with strong belief in personal privacy,security,freedom in traveling and carpooling service constraints would have less potential to use thecarpooling service.In addition,pro-auto and pro-carpooling attitudes,marital status,profession and travel purpose for carpooling are also underlying factors.The findings implicate that to promote carpooling policy it is required to consider appropriate incentives on this service and disincentives on use of private vehicle along with modification of people’s attitudes and intentions.
基金supported by PVST project, funded under the Danish Energiteknologiske Udviklings-og Demonstrationsprogram (EUDP) programme (64017-0041)the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (LAPS21)
文摘The decreasing cost of solar photovoltaics(PVs)and battery storage systems is driving their adoption in the residential distribution system,where more consumers are becoming prosumers.Accompanying this trend is the potential roll-out of home energy management systems(HEMSs),which provide a means for prosumers to respond to externalities such as energy price,weather,and energy demands.However,the economic operation of prosumers can affect grid security,especially when energy prices are extremely low or high.Therefore,it is paramount to design a framework that can accommodate the interests of the key stakeholders in distribution systems—namely,the network operator,prosumer,and aggregator.In this paper,a novel transactive energy(TE)-based operational framework is proposed.Under this frame-work,aggregators interact with the distribution grid operator through a negotiation process to ensure network security,while at the lower level,prosumers submit their schedule to the aggregator through the HEMS.If network security is at risk,aggregators will send an additional price component representing the cost of security(CoS)to the prosumer to stimulate further response.The simulation results show that the proposed framework can effectively ensure the economic operation of aggregators and prosumers in distribution systems while maintaining grid security.
基金Under the auspices of the Ministry of Education of Humanities and Social Science Project(No.17XJA790005)Grant of Shaanxi Social Science Association&Shaanxi Statistics Bureau(No.2019TJ053)。
文摘China has started shifting from relying on supply management to demand management strategy in addressing its water shortage problems.Water option,a financial derivative for water commodity,has been utilized to manage water demands in the United States and Europe since the 1990 s but is still novel to China.In this study we analyzed the pros and cons of China’s existing system for water rights transfers and proposed an alternative,flexible trading instrument-water options for China.Incorporating the uncertainty to water option pricing,this study first conducted an empirical analysis of the water option in the water-receiving area of the Hanjiang-Weihe River Transfer Project of China,and then evaluated the benefits of the water option applications.Results show that water option trading can bring water cost saving and increase the potential industrially added value for industrial enterprises in the receiving area,and trading of short-and-medium term water options is more favorable than the long-term water options trading.The novel water option trading proposed in this study,once verified through pilot studies,will be helpful in addressing water shortage problems in China.
