HVAC-based hierarchical energy management system for microgrids

With the high penetration of renewable energy into the grid, power fluctuations and supply-demand power mismatch are becoming more prominent, which pose a great challenge for the power system to eliminate negative effects through demand side management (DSM). The flexible load, such as heating, ventilation, air conditioning (HVAC) system, has a great potential to provide demand response services in the electricity grids. In this thesis, a comprehensive framework based on a forecasting-management optimization approach is proposed to coordinate multiple HVAC systems to deal with uncertainties from renewable energy resources and maximize the energy efficiency. In the forecasting stage, a hybrid model based on Multiple Aggregation Prediction Algorithm with exogenous variables (MAPAx)-Principal Components Analysis (PCA) is proposed to predict changes of local solar radiance, vy using the local observation dataset and real-time meteorological indexes acquired from the weather forecast spot. The forecast result is then compared with the statistical benchmark models and assessed by performance evaluation indexes. In the management stage, a novel distributed algorithm is developed to coordinate power consumption of HVAC systems by varying the compressors’ frequency to maintain the supply-demand balance. It demonstrates that the cost and capacity of energy storage systems can be curtailed, since HVACs can absorb excessive power generation. More importantly, the method addresses a consensus problem under a switching communication topology by using Lyapunov argument, which relaxes the communication requirement. In the optimization stage, a price-comfort optimization model regarding HVAC’s end users is formulated and a proportional-integral-derivative (PID)-based distributed algorithm is thus developed to minimize the customer’s total cost, whilst alleviating the global power imbalance. The end users are motivated to participate in energy trade through DSM scheme. Furthermore, the coordination scheme can be extended to accommodate battery energy storage systems (BESSs) and a hybrid BESS-HVAC system with increasing storage capacity is proved as a promising solution to enhance its selfregulation ability in a microgrid. Extensive case studies have been undertaken with the respective control strategies to investigate effectiveness of the algorithms under various scenarios. The techniques developed in this thesis has helped the partnership company of this project to develop their smart immersion heaters for the customers with minimum energy cost and maximum photovoltaic efficiency

Energy Management of Grid-Connected Microgrids, Incorporating Battery Energy Storage and CHP Systems Using Mixed Integer Linear Programming

In this thesis, an energy management system (EMS) is proposed for use with battery energy storage systems (BESS) in solar photovoltaic-based (PV-BESS) grid-connected microgrids and combined heat and power (CHP) applications. As a result, the battery's charge/discharge power is optimised so that the overall cost of energy consumed is minimised, considering the variation in grid tariff, renewable power generation and load demand. The system is modelled as an economic load dispatch optimisation problem over a 24-hour time horizon and solved using mixed integer linear programming (MILP) for the grid-connected Microgrid and the CHP application. However, this formulation requires information about the predicted renewable energy power generation and load demand over the next 24 hours. Therefore, a long short-term memory (LSTM) neural network is proposed to achieve this. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the energy management system (EMS) that benefits from using actual generation and demand data in real-time. At each time-step, the LSTM predicts the generation and load data for the next 24 h. The dispatch problem is then solved, and the real-time battery charging or discharging command for only the first hour is applied. Real data are then used to update the LSTM input, and the process is repeated. Simulation results using the Ushant Island as a case study show that the proposed online optimisation strategy outperforms the offline optimisation strategy (with no RH), reducing the operating cost by 6.12%. The analyses of the impact of different times of use (TOU) and standard tariff in the energy management of grid-connected microgrids as it relates to the charge/discharge cycle of the BESS and the optimal operating cost of the Microgrid using the LSTM-MILP-RH approach is evaluated. Four tariffs UK tariff schemes are considered: (1) Residential TOU tariff (RTOU), (2) Economy seven tariff (E7T), (3) Economy ten tariff (E10T), and (4) Standard tariff (STD). It was found that the RTOU tariff scheme gives the lowest operating cost, followed by the E10T tariff scheme with savings of 63.5% and 55.5%, respectively, compared to the grid-only operation. However, the RTOU and E10 tariff scheme is mainly used for residential applications with the duck curve load demand structure. For community grid-connected microgrid applications except for residential-only communities, the E7T and STD, with 54.2% and 39.9%, respectively, are the most likely options offered by energy suppliers. The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system. However, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, a real-time EMS using a combination of LSTM neural networks, MILP, and RH control strategy is proposed. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method. The finding shows that the BESS is a valuable asset for sustainable energy transition. However, they must be operated safely to guarantee operational cost reduction and longer life for the BESS

Multi-agent architecture for local electricity trading in power distribution systems

[ES] En la última década, los mercados eléctricos han desarrollado entornos competitivos para sistemas eléctricos completos. El rápido crecimiento de los recursos energéticos distribuidos ha dificultado mantener la credibilidad y estabilidad del sistema. Sin embargo, debido a la volatilidad de los recursos energéticos distribuidos las estrategias convencionales de gestión de la energía son incapaces de resolver estos problemas de forma centralizada. Además, los mercados centralizados de electricidad no son capaces de adaptarse al comportamiento flexible de los consumidores que ocurre en los programas de respuesta de demanda. Por lo tanto, se requieren nuevas estructuras de comercio de electricidad que proporcionen energía a las redes de distribución de forma descentralizada y distribuida. Este trabajo presenta un enfoque ascendente de gestión energética basado en una arquitectura multiagente para el comercio local de la electricidad. La estructura propuesta consiste en una clase de organización basada en sistemas multiagente, en la cual cada agente cumple diferentes tareas. Estos agentes est_an formados por recursos energéticos distribuidos, consumidores eléctricos, prosumidores, vehículos eléctricos (Electricit Vehicles (EV)), agregadores, un operador del sistema de distribución, coordinadores locales y los coordinadores de los EV del sistema. Además, proponemos un enfoque ascendente para el comercio de energía desde los usuarios finales, como agentes prosumidores capaces de proporcionar transacciones energéticas bidireccionales a los agregadores y al gestor de la red de distribución (Distibution System Operator (DSO)). En este contexto, se presenta una arquitectura basada en sistemas multiagente, para el sistema eléctrico de las casas inteligentes (como ejemplo de usuario final). A continuación, se define el sistema de gestión de la energía en el hogar (HEMS por sus siglas en ingles) para modelar el comportamiento flexible de los usuarios finales residenciales y su incertidumbre basándose en diferentes métodos de optimización (por ejemplo, intervalo, estocástico e intervalo-estocástico). Además, presentamos un método basado en escenarios probabilísticos para la gestión de la energía residencial y el comercio de energía con el mercado local de electricidad basado en una estrategia de licitación óptima. De acuerdo con nuestro modelo de oferta óptimo, el HEMS es capaz de realizar transacciones de energía con otros actores en su vecindario como un agente de fijación de precios basado en los enfoques de intercambio de energía entre pares o enfoques basados en la comunidad. Conforme al enfoque ascendente propuesto en nuestro trabajo de doctorado, las decisiones de los agentes en la capa inferior tienen prioridad en comparación con las decisiones de los agentes en las capas superiores. De esta manera, la estrategia propuesta gestiona la energía localmente para lograr una optimización social global. Además, en la red de distribución se pueden comercializar localmente diferentes tipos de productos básicos de electricidad, como la energía y la flexibilidad. A continuación, hemos propuesto varios enfoques (por ejemplo, descentralizado, monopolístico y basado en juegos) para la gestión de la flexibilidad energética entre los agentes de la red de distribución de energía, teniendo en cuenta el comportamiento flexible de los usuarios finales y los agregadores. Por último, se ha estudiado el impacto de los futuros sistemas de transporte en las redes inteligentes. Así, la gestión de la flexibilidad energética de los usuarios finales y las operaciones de recarga de los vehículos eléctricos se modelan en la red de distribución. Se han presentado tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los vehículos eléctricos entre los actores de la capa inferior del sistema eléctrico. Además, la incertidumbre causada por la movilidad de los vehículos eléctricos se ha modelado mediante una programación estocástica. Aquí, el reto es modelar un problema multinivel basado en la función objetiva de los agentes considerando la incertidumbre de los parámetros estocásticos del sistema. De esta forma, cada agente puede participar en diferentes tipos de transacciones eléctricas según sus funciones objetivas correspondientes. Se evalúa el rendimiento del sistema propuesto de gestión de la energía en el hogar (HEMS) comparándolo con los métodos de optimización de intervalos estocásticos propuestos y de bandas estocásticas predichas medicadas. Evaluamos el impacto del modelo de flexibilidad energética y su exactitud de predicción. Además, evaluamos el programa de respuesta de demanda en términos de las ganancias esperadas, de la energía eléctrica tramitada y de la credibilidad de los resultados. Para ello, proponemos un modelo de oferta óptima para el sistema de gestión de la energía en el hogar. Así, el sistema puede participar en el comercio local de electricidad. El rendimiento del modelo de oferta _optima propuesto se evalúa en dos casos diferentes. El Caso 1 evalúa el impacto de los coeficientes de optimismo y flexibilidad en el HEMS, considerando la estrategia de licitación óptima. En el caso 2, sin embargo, el rendimiento de los dos métodos de optimización diferentes -llamados InterStoch e Hybrid- en el HEMS se evalúa sin considerar la estrategia de licitación _optima. Posteriormente, se evalúa el funcionamiento de nuestros enfoques descentralizados, monopolísticos y basados en juegos en términos de su impacto en la incertidumbre de la línea de distribución y el comportamiento flexible de los usuarios finales. Por último, modelamos la gestión de la flexibilidad energética de los usuarios finales y la operación de carga de los EV en la red de distribución. Se presentan tres estrategias de gestión de la energía para abordar la flexibilidad energética y el funcionamiento de los EV entre los actores de la capa inferior del sistema eléctrico

Distributed Market-Grid Coupling Using Model Predictive Control

In this dissertation, a feedback control concept is proposed for modeling a market-grid coupling. The contributions are fourfold: 1) Identification and characterization of an interoperable control between the power market and the power grid; 2) Design of a closed-loop MPC for the market-grid coupling; 3) Extension of the single control loop with a collaborative distributed MPC strategy for coupling distributed markets and grids; 4) Development of an adaptive load forecasting framework

A review of hierarchical control for building microgrids

Building microgrids have emerged as an advantageous alternative for tackling environmental issues while enhancing the electricity distribution system. However, uncertainties in power generation, electricity prices and power consumption, along with stringent requirements concerning power quality restrain the wider development of building microgrids. This is due to the complexity of designing a reliable and robust energy management system. Within this context, hierarchical control has proved suitable for handling different requirements simultaneously so that it can satisfactorily adapt to building environments. In this paper, a comprehensive literature review of the main hierarchical control algorithms for building microgrids is discussed and compared, emphasising their most important strengths and weaknesses. Accordingly, a detailed explanation of the primary, secondary and tertiary levels is presented, highlighting the role of each control layer in adapting building microgrids to current and future electrical grid structures. Finally, some insights for forthcoming building prosumers are outlined, identifying certain barriers when dealing with building microgrid communities

Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated