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.

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.

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.

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.

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.

Residential electrical vehicle charging strategies:the good,the bad and the ugly

In recent years,a wide variety of centralised and decentralised algorithms have been proposed for residential charging of electric vehicles(EVs).In this paper,we present a mathematical framework which casts the EV charging scenarios addressed by these algorithms as optimisation problems having either temporal or instantaneous optimisation objectives with respect to the different actors in the power system.Using this framework and a realistic distribution network simulation testbed,we provide a comparative evaluation of a range of different residential EV charging strategies,highlighting in each case positive and negative characteristics.

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).

DSM in an area consisting of residential, commercial and industrial load in smart grid

With the latest introduction of the demand side management （DSM） in smart grids, the power distribution units are able to modify the load schedules of the consumers. This involves a co-operative interaction of the utility and the consumers so as to achieve customer load modifications in which the customer, utility and society all are benefited. The interaction is performed with the help of the devices known as the smart meter. This paper shows the use of game theory and logical mathematical expressions in order to achieve the objec- tives. The objectives are to minimize the peak to average ratio （PAR） and the energy cost. The outcome of the game between supplier and customers helps to shape the load profile. The design proposed in this paper is very user- friendly and is based on simple logarithmic programming computations. In this paper, residential, commercial and industrial types of loads are taken into account. A basic 24 h load schedule along with the fluctuating prices at each hour of the day is forecasted by the supplier of the various shiftable and non-shiftable loads and then that schedule is conveyed to the user. The users are encouraged to shift their high load devices to off-peak hours which will not only reduce their electricity costs but also substantially reduce the PAR in the load demand.

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.

Review of Key Problems Related to Integrated Energy Distribution Systems

Integrated energy distribution system(IEDS)is one of the integrated energy and power system forms,which involves electricity/gas/cold/heat and other various energy forms.The energy coupling relationship is close and complex.IEDS is the focus of regional energy internet research and development at home and abroad.Compared with the traditional power distribution system,IEDS through the multi-energy coupling link comprehensive utilization,effectively improve the distribution system economy,safety,reliability,flexibility and toughness,but also to ease the regional energy system environmental pressure.IEDS is an important direction for the future development of energy systems,and its related research and practice on China’s energy system development also has important practical and strategic significance.This paper summarizes the related researches of the IEDS and explores the energy operation characteristics and coupling mechanisms.What’s more,the integrated model of IEDS is summarized.On these bases,this paper discusses and prospects some key issues such as joint planning,optimization control and security analysis,state estimation and situational awareness and generalized demand side management.

Optimal air-conditioning load control in distribution network with intermittent renewables

The coordinated operation of controllable loads,such as air-conditioning load, and distributed generation sources in a smart grid environment has drawn significant attention in recent years. To improve the wind power utilization level in the distribution network and minimize the total system operation costs, this paper proposes a MILP(mixed integer linear programming) based approach to schedule the interruptible air-conditioning loads. In order to mitigate the uncertainties of the stochastic variables including wind power generation, ambient temperature change, and electricity retail price, the rolling horizon optimization(RHO) strategy is employed to continuously update the real-time information and proceed the control window. Moreover, to ensure the thermal comfort of customers, a novel two-parameter thermal model is introduced to calculate the indoor temperature variation more precisely. Simulations on a five node radial distribution network validate the efficiency of the proposed method.

Rolling horizon optimization for real-time operation of thermostatically controlled load aggregator

Thermostatically controlled loads(TCLs)have great potentials to participate in the demand response programs due to their flexibility in storing thermal energy.The two-way communication infrastructure of smart grids provides opportunities for the smart buildings/houses equipped with TCLs to be aggregated in their participation in the electricity markets.This paper focuses on the realtime scheduling of TCL aggregators in the power market using the structure of the Nordic electricity markets a case study.An International Organization of Standardization(ISO)thermal comfort model is employed to well control the occupants’thermal comfort,while a rolling horizon optimization(RHO)strategy is proposed for the TCL aggregator to maximize its profit in the regulation market and to mitigate the impacts of system uncertainties.The simulations are performed by means of a metaheuristic optimization algorithm,i.e.,natural aggregation algorithm(NAA).A series of simulations are conducted to validate the effectiveness of proposed method.

Direct load control by distributed imperialist competitive algorithm

Demand side management techniques have drawn significant attentions along with the development of smart grid.This paper proposes a new direct load control(DLC)model for scheduling interruptible air conditioner loads.The model is coordinated with the unit commitment and economic dispatch to minimize the total operation cost over the whole dispatch horizon.The network constraints are also considered in the model.To ensure the thermal comfort of the occupants,we are among the first to incorporate the advanced two-parameter thermal inertia dynamical model of customer houses into the DLC model to calculate the indoor temperature variation.This paper also proposes a distributed imperialist competitive algorithm to effectively solve the model.The simulation studies prove the efficiency of the proposed methodology.

An overview of selected topics in smart grids

Smart technologies when used in the tradi- tional grid infrastructure will provide a different environ- ment and working conditions in the grid by bringing the required smartness into the grid, called the smart grid. The smart grid can play a major role in the upcoming days to come because there is a necessity to integrate coordinated renewable energy resources into the grid and to operate the grids at a higher efficiency considering many aspects including reliability of the supply. Apart from this, there is a necessity to manage the demand supply gap in the smart grid by optimally scheduling the generators or by effectively scheduling the demand side resources instead of going for the traditional methods like partial or full load shedding. This paper presents an overview on the present state-of-the-art of smart grid technologies and broadly classifies the papers referred into two major areas, papers based on improvement of operational efficiency in smart grids and papers based on smartness in maintaining the demand supply gap. Some of the papers projected in this work also give a brief overview of the necessity of the smart grid.