Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

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.

Decentralized Demand Management Based on Alternating Direction Method of Multipliers Algorithm for Industrial Park with CHP Units and Thermal Storage

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.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

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.

Demand Side Management for Thermally Activated Building Systems Based on Multiple Linear Regression

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%.

Demand side management for solving environment constrained economic dispatch of a microgrid system using hybrid MGWOSCACSA algorithm

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.

Inventory Management and Demand Forecasting Improvement of a Forecasting Model Based on Artificial Neural Networks

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.

Prediction-based Manufacturing Center Self-adaptive Demand Side Energy Optimization in Cyber Physical Systems

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.

A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

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.

Emission Patterns under Alternative Congestion and Motor Vehicle Pollution Mitigation Policies in Shanghai

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.

A Distributed Transactive Energy Mechanism for Integrating PV and Storage Prosumers in Market Operation

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.

Optioning Water Rights:A Potential Alternative to the Hanjiang-Weihe River Water Transfer Project,China

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.

Travelers'attitudes toward carpooling in Lahore:motives and constraints

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.

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario,the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation.Demand side management(DSM)is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives.Consumers are expected to respond(demand response(DR))in various ways to attain these benefits.Nowadays,residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals.In this paper,the use of a smart residential energy management system(SREMS)is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances.Further,the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery(charging/floating/discharging)and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit(CCL).The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.

Mobility driven network slicing: an enabler of on demand mobility management for 5G

As different requirements on mobility support will be introduced by diversified communication scenarios in the fifth generation （5G）, on demand mobility management is put forward to simplify signaling process, reduce terminal power consumption, improve network efficiency and so on. In order to enable on demand mobility management in 5G networks, a mobility driven network slicing （MDNS） was proposed, which takes individual mobility support requirements into account while customizing networks for different mobile services. Within the MDNS framework, the actual levels of required mobility support are determined by a mobility description system, and network slice templates with the corresponding mobility management schemes are defined by a network slice description function. By instantiating the network slices, each mobile terminal could be directed to the network slice with the most appropriate mobility management scheme. Based on this, a prototype was implemented to validate the feasibility of MDNS framework, i.e. creating multiple network slices with different mobility management schemes. In addition, the performance evaluation on average cost of processing a mobility event is conducted for the proposed MDNS framework and the long term evolution （LTE） system, and operating benefits are analyzed including efficiency and scalability.

Microgrid Energy Management: Classification, Review and Challenges

Microgrids provide a way to introduce ecologically acceptable energy production to the power grid.The main chal-lenges with microgrids are overall control,as well as maintaining safe,reliable and economical operation.Researchers explore implementing these possibilities,but in rapidly expanding areas of research there is always a need to review what has been done so far and give guidelines to new researchers on topics in need of more exploration.This paper offers an extensive literature review of the energy management part of the microgrid control system.Based on extensive literature research,the authors of this article offer their view on energy management system orga-nization.Review through centralized and decentralized structure is given.The most popular research topic is the optimization of energy management.This paper offers a new perspective on the classification of optimization methods used for microgrid energy management,listing and sorting many problem related refer-ences.The microgrid is not an assembly of independent elements but rather a coordinated system of intertwined functions.These elements of microgrid functioning,like energy storage systems,demand side management.Electric vehicles are also explored in this paper,giving the current state of their research through references.Index Terms-Demand side management,electric vehicles,energy management system,energy storage systems,microgrid,operation and control.

Personalized real-time pricing for efficient and fair demandresponse in energy cooperatives and highly competitive flexibility markets

This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.

Integrated Optimization of Smart Home Appliances with Cost-effective Energy Management System

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load demand.In this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial customers.The optimization problem is developed under the time of use pricing(TOUP)scheme.To optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the appliances.For analysis,eight different cases are considered under various time scheduling algorithms.

Critical peak rebate strategy and application to demand response

Time-of-use (TOU) pricing strategy is an important component of demand-side management (DSM), but the cost of supplying power during critical peak periods remains high under TOU prices. This affects power system reliability. In addition, TOU prices are usually applicable to medium- and long-term load control but cannot effectively regulate short-term loads. Therefore, this paper proposes an optimization method for TOU pricing and changes the electricity consumption patterns during the critical peak periods through a critical peak rebate (CPR). This reduces generation costs and improves power system reliability. An optimization model for peak-flat-valley (PFV) period partition is established based on fuzzy clustering and an enumeration iterative technique. A TOU pricing optimization model including grid-side and customer-side benefits is then proposed, and a simulated annealing particle swarm optimization (SAPSO) algorithm is used to solve the problem. Finally, a CPR decision model is developed to further reduce critical peak loads. The effectiveness of the proposed model and algorithm is illustrated through different case studies of the Roy Billinton Test System (RBTS).

Nodal user’s demand response based on incentive based programs

This paper describes a practical approach to identify nodal price compensation payment for nodal consumers willing to reduce their energy consumption(consumers’ demand response). The implementation of a nodal reliability service pricing is based on contingency assessment of N-2 order for transmission lines. A representative annualized demand curve is used to reflect the system’s operation condition by seasons. Such curve is used to access the nodal reliability impact trough a whole year in order to determine back-payments(incentive payment) to users for service interruption. The IEEE_RTS 24 nodes system is used to implement the proposed approach.

Guest editorial:special section on managing electricity demand

The interest in managing electricity demand surfaced in earnest during the 1970s as economic,political,social,technological,and resource supply factors combined to change the electricity sectors’operating environment and its outlook for the future.Ever since then,a successive series of concepts have evolved as an effective way of mitigating these risks including:demand-side management(DSM),demand response(DR),and transactive energy.