Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Modelling and Simulation Approaches for Local Energy Community Integrated Distribution Networks

Due to the absence of studies of local energy communities (LECs) where the grid is represented, it is very difficult to infer implications of increased LEC integration for the distribution grid as well as for the wider society. Therefore, this paper aims to investigate holistic modelling and simulation approaches of LECs. To conduct a quantifiable assessment of different control architectures, LEC types and market frameworks, a flexible and comprehensive LEC modelling and simulation approach is needed. Modelling LECs and the environment they operate in involves a holistic approach consisting of different layers: market, controller, and grid. The controller layer is relevant both for the overall energy management system of the LEC and the controllers of single components in a LEC. In this paper, the different LEC modelling approaches in the reviewed literature are presented, several multilayered concepts for LECs are proposed, and a case study is presented to illustrate a holistic simulation where the different layers interact.Modelling and Simulation Approaches for Local Energy Community Integrated Distribution NetworkspublishedVersio

Market model for clustered microgrids optimisation including distribution network operations

This paper proposes a market model for the purpose of optimisation of clustered but sparse microgrids (MGs). The MGs are connected with the market by distribution networks for the sake of energy balance and to overcome emergency situations. The developed market structure enables the integration of virtual power plants (VPPs) in energy requirement of MGs. The MGs, internal service providers (ISPs), VPPs and distribution network operator (DNO) are present as distinct entities with individual objective of minimum operational cost. Each MG is assumed to be present with a commitment to service its own loads prior to export. Thus an optimisation problem is formulated with the core objective of minimum cost of operation, reduced network loss and least DNO charges. The formulated problem is solved by using heuristic optimization technique of Genetic Algorithm. Case studies are carried out on a distribution system with multiple MGs, ISP and VPPs which illustrates the effectiveness of the proposed market optimisation strategy. The key objective of the proposed market model is to coordinate the operation of MGs with the requirements of the market with the help of the DNO, without decreasing the economic efficiency for the MGs nor the distribution network. © The Institution of Engineering and Technology 2019

Distributed Control Strategies for Microgrids: An Overview

There is an increasing interest and research effort focused on the analysis, design and implementation of distributed control systems for AC, DC and hybrid AC/DC microgrids. It is claimed that distributed controllers have several advantages over centralised control schemes, e.g., improved reliability, flexibility, controllability, black start operation, robustness to failure in the communication links, etc. In this work, an overview of the state-of-the-art of distributed cooperative control systems for isolated microgrids is presented. Protocols for cooperative control such as linear consensus, heterogeneous consensus and finite-time consensus are discussed and reviewed in this paper. Distributed cooperative algorithms for primary and secondary control systems, including (among others issues) virtual impedance, synthetic inertia, droop-free control, stability analysis, imbalance sharing, total harmonic distortion regulation, are also reviewed and discussed in this survey. Tertiary control systems, e.g., for economic dispatch of electric energy, based on cooperative control approaches, are also addressed in this work. This review also highlights existing issues, research challenges and future trends in distributed cooperative control of microgrids and their future applications

Self-organising multi-agent control for distribution networks with distributed energy resources

Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time.Recent years have seen an increase in the connection of dispersed distributed energy resources (DERs) and advanced control and operational components to the distribution network. These DERs can come in various forms, including distributed generation (DG), electric vehicles (EV), energy storage, etc. The conditions of these DERs can be varying and unpredictably intermittent. The integration of these distributed components adds more complexity and uncertainty to the operation of future power networks, such as voltage, frequency, and active/reactive power control. The stochastic and distributed nature of DGs and the difficulty in predicting EV charging patterns presents problems to the control and management of the distribution network. This adds more challenges to the planning and operation of such systems. Traditional methods for dealing with network problems such as voltage and power control could therefore be inadequate. In addition, conventional optimisation techniques will be difficult to apply successfully and will be accompanied with a large computational load. There is therefore a need for new control techniques that break the problem into smaller subsets and one that uses a multi-agent system (MAS) to implement distributed solutions. These groups of agents would coordinate amongst themselves, to regulate local resources and voltage levels in a distributed and adaptive manner considering varying conditions of the network. This thesis investigates the use of self-organising systems, presenting suitable approaches and identifying the challenges of implementing such techniques. It presents the development of fully functioning self-organising multi-agent control algorithms that can perform as effectively as full optimization techniques. It also demonstrates these new control algorithms on models of large and complex networks with DERs. Simulation results validate the autonomy of the system to control the voltage independently using only local DERs and proves the robustness and adaptability of the system by maintaining stable voltage control in response to network conditions over time

Internet of things (IoT) based adaptive energy management system for smart homes

PhD ThesisInternet of things enhances the flexibility of measurements under different environments, the development of advanced wireless sensors and communication networks on the smart grid infrastructure would be essential for energy efficiency systems. It makes deployment of a smart home concept easy and realistic. The smart home concept allows residents to control, monitor and manage their energy consumption with minimal wastage. The scheduling of energy usage enables forecasting techniques to be essential for smart homes. This thesis presents a self-learning home management system based on machine learning techniques and energy management system for smart homes. Home energy management system, demand side management system, supply side management system, and power notification system are the major components of the proposed self-learning home management system. The proposed system has various functions including price forecasting, price clustering, power forecasting alert, power consumption alert, and smart energy theft system to enhance the capabilities of the self-learning home management system. These functions were developed and implemented through the use of computational and machine learning technologies. In order to validate the proposed system, real-time power consumption data were collected from a Singapore smart home and a realistic experimental case study was carried out. The case study had proven that the developed system performing well and increased energy awareness to the residents. This proposed system also showcases its customizable ability according to different types of environments as compared to traditional smart home models. Forecasting systems for the electricity market generation have become one of the foremost research topics in the power industry. It is essential to have a forecasting system that can accurately predict electricity generation for planning and operation in the electricity market. This thesis also proposed a novel system called multi prediction system and it is developed based on long short term memory and gated recurrent unit models. This proposed system is able to predict the electricity market generation with high accuracy. Multi Prediction System is based on four stages which include a data collecting and pre-processing module, a multi-input feature model, multi forecast model and mean absolute percentage error. The data collecting and pre-processing module preprocess the real-time data using a window method. Multi-input feature model uses single input feeding method, double input feeding method and multiple feeding method for features input to the multi forecast model. Multi forecast model integrates long short term memory and gated recurrent unit variations such as regression model, regression with time steps model, memory between batches model and stacked model to predict the future generation of electricity. The mean absolute percentage error calculation was utilized to evaluate the accuracy of the prediction. The proposed system achieved high accuracy results to demonstrate its performance

Enabling cooperative and negotiated energy exchange in remote communities

Energy poverty at the household level is defined as the lack of access to electricity and reliance on the traditional use of biomass for cooking, and is a serious hindrance to economic and social development. It is estimated that 1.3 billion people live without access to electricity and almost 2.7 billion people rely on biomass for cooking, a majority of whom live in small communities scattered over vast areas of land (mostly in the Sub-Saharan Africa and the developing Asia). Access to electricity is a serious issue as a number of socio-economic factors, from health to education, rely heavily on electricity. Recent initiatives have sought to provide these remote communities with off-grid renewable microgeneration infrastructure such as solar panels, and electric batteries. At present, these resources (i.e., microgeneration and storage) are operated in isolation for individual home needs, which results in an inefficient and costly use of resources, especially in the case of electric batteries which are expensive and have a limited number of charging cycles. We envision that by connecting homes together in a remote community and enabling energy exchange between them, this microgeneration infrastructure can be used more efficiently. Against this background, in this thesis we investigate the methods and processes through which homes in a remote community can exchange energy. We note that remote communities lack general infrastructure such as power supply systems (e.g., the electricity grid) or communication networks (e.g., the internet), that is taken for granted in urban areas. Taking these challenges into account and using insights from knowledge domains such game theory and multi-agent systems, we present two solutions: (i) a cooperative energy exchange solution and (ii) a negotiated energy exchange solution, in order to enable energy exchange in remote communities.Our cooperative energy exchange solution enables connected homes in a remote community to form a coalition and exchange energy. We show that such coalition a results in two surpluses: (i) reduction in the overall battery usage and (ii) reduction in the energy storage losses. Each agents's contribution to the coalition is calculated by its Shapley value or, by its approximated Shapley value in case of large communities. Using real world data, we empirically evaluate our solution to show that energy exchange: (i) can reduce the need for battery charging (by close to 65%) in a community; compared with when they do not exchange energy, and (ii) can improve the efficient use of energy (by up to 10% under certain conditions) compared with no energy exchange. Our negotiated energy exchange solution enables agents to negotiate directly with each other and reach energy exchange agreements. Negotiation over energy exchange is an interdependent multi-issue type of negotiation that is regarded as very difficult and complex. We present a negotiation protocol, named Energy Exchange Protocol (EEP), which simplifies this negotiation by restricting the offers that agents can make to each other. These restrictions are engineered such that agents, negotiation under the EEP, have a strategy profile in subgame perfect Nash equilibrium. We show that our negotiation protocol is tractable, concurrent, scalable and leads to Pareto-optimal outcomes (within restricted the set of offers) in a decentralised manner. Using real world data, we empirically evaluate our protocol and show that, in this instance, a society of agents can: (i) improve the overall utilities by 14% and (ii) reduce their overall use of the batteries by 37%, compared to when they do not exchange energy

Networked Multiagent Safe Reinforcement Learning for Low-carbon Demand Management in Distribution Network

This paper proposes a multiagent based bi-level operation framework for the low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the aggregate load agents optimize the control signals for various types of loads to maximize the profits; in the lower level, the distribution network operator makes optimal dispatching decisions to minimize the operational costs and calculates the distribution locational marginal price and carbon intensity. The distributed flexible load agent has only incomplete information of the distribution network and cooperates with other agents using networked communication. Finally, the problem is formulated into a networked multi-agent constrained Markov decision process, which is solved using a safe reinforcement learning algorithm called consensus multi-agent constrained policy optimization considering the carbon emission allowance for each agent. Case studies with the IEEE 33-bus and 123-bus distribution network systems demonstrate the effectiveness of the proposed approach, in terms of satisfying the carbon emission constraint on demand side, ensuring the safe operation of the distribution network and preserving privacy of both sides.Comment: Submitted to IEEE Transactions on Sustainable Energ