Non-Cooperative Differential Game Based Energy Consumption Control for Dynamic Demand Response in Smart Grid

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users’ energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

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.

Intelligent Load Management Scheme for a Residential Community in Smart Grids Network Using Fair Emergency Demand Response Programs

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modelling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent load management scenario.

Transactive Demand Response Operation at the Grid Edge using the IEEE 2030.5 Standard

This paper presents a transactive demand response(TDR)scheme for a network of residential customers with generation assets that emphasizes interoperability within a transactive energy architecture.A complete laboratory-based implementation provides the first(to our knowledge)realization of a comprehensive TDR use case that is fully compliant with the Institute of Electrical and Electronics Engineers(IEEE)2030.5 standard,which addresses interoperability within a cybersecure smart energy profile(SEP)context.Verification is provided by a full system integration with commercial hardware using Internet Protocol(IP)-based(local area network(LAN)and Wi-Fi)communication protocols and transport layer security(TLS)1.2 cryptographic protocol,and validation is provided by emulation using extensive residential smart meter data.The demand response(DR)scheme is designed to accommodate privacy concerns,allows customers to select their DR compliance level,and provides incentives to maximize their participation.The proposed TDR scheme addresses privacy through the implementation of the SEP 2.0 messaging protocol between a transactive agent(TA)and home energy management system(HEMS)agents.Customer response is handled by a multi-input multi-output(MIMO)fuzzy controller that manages negotiation between the customer agent and the TA.We take a multi-agent system approach to neighborhood coordination,with the TA servicing multiple residences on a common transformer,and use a reward mechanism to maximize customer engagement during the event-based optimization.Based on a set of smart meter data acquired over an extended time period,we engage in multiple TDR scenarios,and demonstrate with a fully-functional IEEE 2030.5-compliant implementation that our scheme can reduce network peak power consumption by 22%under realistic conditions.

Ten questions concerning integrating smart buildings into the smart grid

Recent advances in information and communications technology(ICT) have initiated development of a smart electrical grid and smart buildings. Buildings consume a large portion of the total electricity production worldwide, and to fully develop a smart grid they must be integrated with that grid. Buildings can now be"prosumers"on the grid(both producers and consumers), and the continued growth of distributed renewable energy generation is raising new challenges in terms of grid stability over various time scales. Buildings can contribute to grid stability by managing their overall electrical demand in response to current conditions. Facility managers must balance demand response requests by grid operators with energy needed to maintain smooth building operations.For example, maintaining thermal comfort within an occupied building requires energy and, thus an optimized solution balancing energy use with indoor environmental quality(adequate thermal comfort, lighting, etc.) is needed. Successful integration of buildings and their systems with the grid also requires interoperable data exchange. However, the adoption and integration of newer control and communication technologies into buildings can be problematic with older legacy HVAC and building control systems.Public policy and economic structures have not kept up with the technical developments that have given rise to the budding smart grid, and further developments are needed in both technical and non-technical areas.

Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Control of Indoor Swimming Pools with Potential for Demand Response

Buildings with indoor swimming pools are recognized as very high-energy consumers and present a great potential for electrical and thermal energy savings. A BEMS （building energy management system） could be conceived in order to optimize the building energy demand and with smart grid interaction. This paper presents the condition and potential contract-based demand side response in indoor swimming pools context. The BEMS designed by the authors implements control strategies for HVAC （heating, ventilation and air conditioning） and pumping system in order to reduce the electricity demand during peak hours or in response to an emergency signal from the system operator in critical times. The control strategies for HVAC was carried out by Building Thermal Simulation and the used of a theoretical formula for pumping system, strategies can carry out a significant reduction in power demand both in HVAC and pumping systems.

Privacy-Preserving Prepayment Based Power Request and Trading in Smart Grid

Demand response has been intensively studied in recent years. It can motivate customers to change their consumption patterns according to the dynamic(time-varying) electricity price, which is considered to be the most cost-effective and reliable solution for smoothing the demand curve. However, many existing schemes, based on users' demand request in each period, require users to consume their requested electricity exactly, which sometimes causes inconvenience and losses to the utility, because customers cannot always be able to consume the accurate electricity demand due to various personal reasons. In this paper, we tackle this problem in a novel approach. Instead of charging after consumption, we adopt the prepayment mechanism to implement power request. Furthermore, we propose a trading market running by the control center to cope with the users' dynamic demand. It is noteworthy that both users' original demand and trading records are protected against potential adversaries including the curious control center. Through the numerical simulation, we demonstrate that our scheme is highly efficient in both computation and communication.

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.

Artificial Neural Network-Based Development of an Efficient Energy Management Strategy for Office Building

In the current context,a smart grid has replaced the conventional grid through intelligent energy management,integration of renewable energy sources(RES)and two-way communication infrastructures from power gen-eration to distribution.Energy management from the distribution side is a critical problem for balancing load demand.A unique energy manage-ment strategy(EMS)is being developed for office building equipment.That includes renewable energy integration,automation,and control based on the Artificial Neural Network(ANN)system using Matlab Simulink.This strategy reduces electric power consumption and balances the load demand of the traditional grid.This strategy is developed by taking inputs from an office building electricity consumption behavior study,a power generation study of a solar photovoltaic system,and the supply pattern of a grid in peak and non-peak hours.All this is done in consideration of the Indian scenario,where real-time data of month-wise ANN-based intelligent switching has been established for intermittent renewable sources and peak load reduction,as well as average load reduction,has been demonstrated along with the power control loop without the battery system.

Demand Responsive Market Decision-Makings and Electricity Pricing Scheme Design in Low-Carbon Energy System Environment

The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.

Efficient Energy Performance within Smart Grid

The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. Energy/power plays a critical role for social, economic and industrial development. Because of industrial generalization, especially in agricultural and economical activities, the energy demand has increased rapidly in developed countries. Generation and usage of energy has direct impact on modern power grid. In this scenario energy management is a hard task because load is dynamic and we don’t have control over it. Renewable or undepleted energy resources have great applications and impact in current electric power system situation. For example it gives pollution free (green) energy which is environment and user friendly. It is cost effective;it uses natural resources for its generation and hence do not waste any coal, gas etc. There are many inducements to empower energy productivity. As current smart grid is complex and non linear in operation and design, it used an optimized method that provides maximum efficiency with minimum input. Our work depicts a case study of hybrid electric aircraft for achieving high performance.

FEDRP Based Model Implementation of Intelligent Energy Management Scheme for a Residential Community in Smart Grids Network

In the framework of liberalized deregulated electricity market, dynamic competitive environment exists between wholesale and retail dealers for energy supplying and management. Smart Grids topology in form of energy management has forced power supplying agencies to become globally competitive. Demand Response (DR) Programs in context with smart energy network have influenced prosumers and consumers towards it. In this paper Fair Emergency Demand Response Program (FEDRP) is integrated for managing the loads intelligently by using the platform of Smart Grids for Residential Setup. The paper also provides detailed modeling and analysis of respective demands of residential consumers in relation with economic load model for FEDRP. Due to increased customer’s partaking in this program the load on the utility is reduced and managed intelligently during emergency hours by providing fair and attractive incentives to residential clients, thus shifting peak load to off peak hours. The numerical and graphical results are matched for intelligent energy management scenario.

Standardization of Smart Grid Customer Interfaces

One of the key concepts of a future "smart grid" is to combine modern communication technology with an improved electric grid to enable energy consumers to exchange information with energy suppliers in order to collaboratively manage electricity supply and demand.This information exchange can be used to reduce the stress on the grid during times of peak demand,enable the expanded use of intermittent renewable energy sources,improve resilience during weather related outages,and integrate customer-owned generation capabilities with grid supplied electricity.Standards are needed to define information exchange interfaces between energy service providers and their customers.This paper describes requirements for those interfaces and emerging standards in the United States that address these issues.

Smart Energy Resource Grid in China:What's the Path?

Initiated and approved in 2009,the project of Development Pattern and Implementation Design of Smart Energy Resource Grid in China is now accomplished with the research achievement released to the public and highly valued by authorized organizations such as the Global Smart Grid Federation.In this paper,based on the description of the research achievement,the advantages of the smart energy resource grid in China and the consequential changes are analyzed and discussed,involving the industries of electric power,oil and gas,energy storage,water supply,architecture and transportations etc.

Some Hotspots Concerned with Smart Grid

The smart grid has been such a hot topic recently.In this paper the hot topics in this field,such as the definition and features of smart grid,key technical problems to be addressed such as new system components,new types of transducers and measurement technologies,advanced interfaces,event-driven fast-simulated decision-making and coordination,and adaptive control,etc.,and diff iculties are analyzed and discussed.

Data-driven model predictive control for power demand management and fast demand response of commercial buildings using support vector regression

Demand response(DR)of commercial buildings by directly shutting down part of operating chillers could provide an immediate power reduction for power grids.In this special fast DR event,effective control needs to guarantee expected power reduction and ensure an acceptable indoor environment.This study,therefore,developed a data-driven model predictive control(MPC)using support vector regression(SVR)for fast DR events.According to the characteristics of fast DR events,the optimized hyperparameters of SVR and shortened searching range of genetic algorithm are used to improve the control performance.Meanwhile,a comprehensive comparison with RC-based MPC is conducted based on three scenarios of power demand controls.Test results show that the proposed SVR-based MPC could fulfill the control objectives of power demand and indoor temperature simultaneously.Compared with RC-based MPC,the SVR-based MPC could alleviate the time/labor cost of model development without sacrificing the control performance of fast DR events.

A Prepaid Smart Metering Scheme Based on WiMAX Prepaid Accounting Model

Prepaid energy meters have been widely adopted by utilities in different countries across the world as an innovative solution to the problem of affordability and consumption management. However, the present smart card based systems have some inherent problems like added cost, low availability and lack of security. In the future Smart Grid paradigm, use of smart meters can completely overhaul these prepaid systems by introducing centralized accounting, monitoring and credit-control functions using state-of-the-art telecommunication technologies like WiMAX. In this paper we pro-pose a prepaid smart metering scheme for Smart Grid application based on centralized authentication and charging using the WiMAX prepaid accounting model. We then discuss its specific application to Demand Response and Roam-ing of Electrical Vehicles.

A Novel Model of Intelligent Electrical Load Management by Goal Programming for Smart Houses, Respecting Consumer Preferences

Energy management is being highly regarded throughout the world. High-energy consumption in residential buildings is one of the dominant reasons of excessive energy consumption. There are many recent works on the demand-side management (DSM) and smart homes to keep control on electricity consumption. The paper is an intelligence to modify patterns, by proposing a time scheduling consumers, such that they can maintain their welfare while saving benefits from time varying tariffs;a model of household loads is proposed;constraints, including daily energy requirements and consumer preferences are considered in the framework, and the model is solved using mixed integer linear programming. The model is developed for three scenarios, and the results are compared: the 1st scenario aims Peak Shaving;the 2nd minimizes Electricity Cost, and the 3rd one, which distinguishes this study from the other related works, is a combination of the 1st and 2nd Scenarios. Goal programming is applied to solve the 3rd scenario. Finally, the best schedules for household loads are presented by analyzing power distribution curves and comparing results obtained by these scenarios. It is shown that for the case study of this paper with the implementation of 3rd scenario, it is possible to gain 7% saving in the electricity cost without any increasing in the lowest peak power consumption.