Demand Response Management and Control Strategies for Integrated Smart Electricity Networks

Abstract

Demand Response (DR) programs are being introduced by some electricity grid operators as resource options for curtailing and reducing the demand of electricity during certain time periods for balancing supply and demand. DR is considered as a class of demand-side management programs, where utilities offer incentives to end-users to reduce their power consumption during peak periods. DR is, indeed, a promising opportunity for consumers to control their energy usage in response to electricity tariffs or other incentives from their energy suppliers. Thus, successful execution of a DR program requires the design of efficient algorithms and strategies to be used in the utility grid to motivate end-users to actively engage in residential DR. This thesis studies DR management using machine learning techniques such as Reinforcement Learning (RL), Fuzzy Logic (FL) and Neural Networks (NN) to develop a Home Energy Management System (HEMS) for customers, construct an energy customer behaviour framework, investigate the integration of Electrical Vehicles (EVs) into DR management at the home level and the provision of ancillary services to the utility grid such as Frequency Regulation (FR), and build effective pricing strategies for Peer-to-Peer (P2P) energy trading. In this thesis, we firstly proposed a new and effective algorithm for residential energy management system using Q-learning method to minimise the electricity bills and maximise the user’s satisfaction. The proposed DR algorithm aims to schedule household appliances considering dynamic electricity prices and different household power consumption patterns. Moreover, a human comfort-based control approach for HEMS has been developed to increase the user’s satisfaction as much as possible while responding to DR schemes. The simulation results presented in this Chapter showed that the proposed algorithm leads to minimising energy consumption, reducing household electricity bills, and maximising the user’s satisfaction. Secondly, with the increasing electrification of vehicles, emerging technologies such as Vehicle-to-Grid (V2G) and Vehicle-to-Home (V2H) have the potential to offer a broad range of benefits and services to achieve more effective management of electricity demand. In this way, EVs become as distributed energy storage resources and can conceivably, in conjunction with other electricity storage solutions, contribute to DR and provide additional capacity to the grid when needed. Therefore, we proposed an effective DR approach for V2G and V2H energy management using Reinforcement Learning (RL) to make optimal decisions to charge or delay the charging of the EV battery pack and/or dispatch the stored electricity back to the grid without compromising the driving needs. Simulations studies are presented to demonstrate how the proposed DR strategy can effectively manage the charging/discharging schedule of the EV battery and how V2H and V2G can contribute to smooth the household load profile, minimise electricity bills and maximise revenue. In addition, the potential benefits of EVs battery and V2G technology to provide grid frequency response services have also been investigated. We have designed an optimal real-time V2G control strategy for EVs to perform supplementary frequency regulation using Deep Deterministic Policy Gradient (DDPG). The main feature that distinguishes the proposed approach from previous related works is that the scheduled charging power of an individual EV is optimally tracked and adjusted in real-time to fulfil the charging demand of EV's battery at the plug-out time without using the forced charging technique to maximise the frequency regulation capacity. Finally, a Peer-to-Peer (P2P) model for energy transaction in a community microgrid has been proposed. The concept of P2P energy trading can promote the implementation of DR by providing consumers with greater control over their energy usage, incentivising them to manage their energy consumption patterns in response to changes in energy supply and demand. It also stimulates the adoption of renewable energy sources. The proposed P2P energy-sharing mechanism for a residential microgrid with price-based DR is designed to engage individual customers to participate in energy trading and ensures that not a single household would be worse off. The proposed pricing mechanism is compared with three popular P2P energy sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and Bill Sharing (BS) considering different types of peers equipped with solar Photovoltaic (PV) panels, EVs, and domestic energy storage systems. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.