Demand-Side Flexibility in Power Systems:A Survey of Residential, Industrial, Commercial, and Agricultural Sectors

In recent years, environmental concerns about climate change and global warming have encouraged countries to increase investment in renewable energies. As the penetration of renewable power goes up, the intermittency of the power system increases. To counterbalance the power fluctuations, demand-side flexibility is a workable solution. This paper reviews the flexibility potentials of demand sectors, including residential, industrial, commercial, and agricultural, to facilitate the integration of renewables into power systems. In the residential sector, home energy management systems and heat pumps exhibit great flexibility potential. The former can unlock the flexibility of household devices, e.g., wet appliances and lighting systems. The latter integrates the joint heat–power flexibility of heating systems into power grids. In the industrial sector, heavy industries, e.g., cement manufacturing plants, metal smelting, and oil refinery plants, are surveyed. It is discussed how energy-intensive plants can provide flexibility for energy systems. In the commercial sector, supermarket refrigerators, hotels/restaurants, and commercial parking lots of electric vehicles are pointed out. Large-scale parking lots of electric vehicles can be considered as great electrical storage not only to provide flexibility for the upstream network but also to supply the local commercial sector, e.g., shopping stores. In the agricultural sector, irrigation pumps, on-farm solar sites, and variable-frequency-drive water pumps are shown as flexible demands. The flexibility potentials of livestock farms are also surveyed

Programação Robusta de Energia para Edifícios Inteligentes considerando a Incerteza em Veículos Eléctricos

Nos últimos anos, o consumo de energia tem aumentado juntamente com o crescimento económico e populacional, onde os edifícios representam um dos principais consumidores. Contudo, surgem preocupações a nível ambiental as quais inspiram governos a concentrarse na concepção de edifícios inteligentes com sistemas de gestão de energia que controlam as fontes de energia renováveis. No entanto, um fator importante a considerar ao lidar com os recursos energéticos é a natureza incerta do seu comportamento. De forma a dar resposta a este desafio, esta tese consiste em propor uma programação ótima dos recursos energéticos baseado na otimização robusta, tendo em conta as incertezas associadas aos veículos elétricos. A otimização robusta é uma abordagem inovadora e eficaz para resolver problemas de otimização que envolvem incerteza, uma vez que encontra a melhor solução entre os piores cenários possíveis. Inicialmente é formulada uma técnica de Redes Neuronais Artificiais, de modo a lidar com as incertezas. Posteriormente, um problema de Programação Linear Binária é estipulado para reduzir os custos energéticos do edíficio sem considerar incertezas. Numa fase final, o modelo determinístico é transformado num problema robusto, assegurando imunidade contra a incerteza associada aos veículos elétricos. De modo a simular o modelo de Otimização Robusta foram implementados três cenários diferentes de programação energética com um horizonte de tempo curto. Os resultados apresentaram uma redução de 14.86% no caso do estado da carga inicial, de 6.75% para a hora de chegada e de 14.18% para a hora de partida, revelando que o modelo implementado permite minimizar os custos totais de eletricidade de um edifício, bem como reduzir os problemas associados à incerteza dos veículos elétricos. Além disso, é demonstrado o ajustamento da técnica de otimização robusta de acordo com vários níveis de robustez.In recent years, electricity consumption has increased along with economic and population growth, with buildings representing one of the main consumers. However, environmental concerns are emerging and inspiring governments to focus on designing intelligent buildings with energy management systems that control renewable energy sources. However, an important factor to consider when dealing with energy resources is the uncertain nature of their behavior. To address this challenge, this thesis proposes optimal scheduling of energy resources based on robust optimization, taking into account the uncertainties associated with electric vehicles. Robust optimization is an innovative and effective approach for solving optimization problems involving uncertainty since it finds the best solution among the worst-case scenarios. Initially, an Artificial Neural Networks technique is formulated to deal with uncertainties. Afterward, a Binary Linear Programming problem is stipulated to reduce the energy costs of the building without considering uncertainties. In the final step, the deterministic model is transformed into a robust problem, ensuring immunity against the uncertainty related to electric vehicles. To simulate the Robust Optimization model, three different energy scheduling scenarios with a short time horizon were implemented. The results showed a reduction of 14.86% for the initial State Of Charge (SoC), 6.75% for the arrival time, and 14.18% for the departure time, revealing that the implemented model allows for minimizing the total electricity costs of a building, as well as reducing the problems associated with the uncertainty of electric vehicles. In addition, the adjustment of the robust optimization technique according to various levels of robustness is demonstrated

Energy resource management in smart buildings considering photovoltaic uncertainty

O aumento do consumo energético em edifícios residenciais tem levado a um maior foco nos métodos de eficiência energética. Deste modo, surge um sistema de gestão de energia residencial que poderá permitir controlar os recursos energéticos em pequena escala dos edifícios, levando a uma diminuição significativa dos custos energéticos através de um escalonamento eficiente. No entanto, a natureza intermitente das fontes de energia renováveis resulta num problema complexo. Para resolver este desafio, esta tese propõe um escalonamento energético baseado na otimização robusta, considerando a incerteza relacionada com a produção fotovoltaica. A otimização robusta é um método emergente e eficaz para lidar com a incerteza e apresenta soluções ótimas considerando o pior cenário da incerteza, ou seja, encontra a melhor solução entre todos os piores cenários possíveis. Um problema de Programação Linear Binária é inicialmente formulado para minimizar os custos do escalonamento energético. De seguida, o objetivo desta tese é transformar o modelo determinístico num problema robusto equivalente para proporcionar-lhe imunidade contra a incerteza associada à produção fotovoltaica. O modelo determinístico é, assim, transformado num modelo do pior cenário possível. Para validar a eficiência e a eficácia do modelo, a metodologia proposta foi implementada em dois cenários sendo cada um deles constituído por três casos de estudo de escalonamento de energia, para um horizonte de escalonamento a curto prazo. Os resultados da simulação demonstram que a abordagem robusta consegue, efetivamente, minimizar os custos totais de eletricidade do edifício, mitigando, simultaneamente, os obstáculos referentes à incerteza relacionada com a produção fotovoltaica. É também demonstrado que a estratégia desenvolvida permite o ajustamento do escalonamento dos recursos energéticos do edifício de acordo com o nível de robustez selecionado.The increase of energy demand in residential buildings has led to a higher focus on energy efficiency methods. This way, the home energy management system arises to control small-scale energy resources on buildings allowing a significant electricity bill decrease throughout efficient scheduling. However, the intermittent and uncertain nature of renewable energy sources results in a complex problem. To solve this challenge, this thesis proposes robust optimization-based scheduling considering the uncertainty in solar generation. Robust Optimization is a very recent and effective technique to deal with uncertainty and provides optimal solutions for the worst-case realization of the uncertain parameter, i.e., it finds the best solution among all the worst scenarios. A Mixed Binary Linear Programming problem is initially formulated to minimize the costs of the energy resource scheduling. Then, this thesis's purpose is to transform the deterministic model into a trackable robust counterpart problem to provide immunity against the photovoltaic output uncertainty. The deterministic model is transformed into the worst-case model. To validate the model’s efficiency and effectiveness, the proposed methodology was implemented in two scenarios with three different energy scheduling case studies for a short-term scheduling horizon. The simulation results demonstrate that the robust approach can effectively minimize the electricity costs of the building while mitigating the drawbacks associated with solar uncertainty. It also proves that the proposed strategy adjusts the energy scheduling according to the selected robustness level

Frequency Management Strategies for Local Power Generation Network

This paper presents an intelligent load frequency control technique based on ANFIS controller which is capable to restore system frequency within small fraction of time. Frequency deviations in microgrid occur when the system supply is not sufficient to match the demand. Efforts are required to keep the frequency deviation within acceptable limit. Using vehicle-to-grid technology, where electric vehicles are used as energy storage elements for load frequency control in microgrid. For generating the control action to electric vehicles and energy sources in microgrid, type-2 ANFIS has been employed for quick frequency stabilization in the presence of load and source disturbances. Diesel generator and wind generator are DG sources considered in this paper and electric vehicles are used as energy storage element. Optimal power sharing among the different generating units and electric vehicles is achieved by ANFIS controller. Adaptive nature of ANFIS makes it more suitable and highly robust controller for a complex inter-connected system. Simulation results demonstrate that ANFIS controller is highly efficient as compared to PID controller, fuzzy logic controller, and interval type-2 fuzzy logic controller

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Holistic approach for microgrid planning and operation for e-mobility infrastructure under consideration of multi-type uncertainties

Integrating renewable energys ources in sectors such as electricity, heat, and transportation must be structured in an economic, technological, and emission- efficient manner to address global environmental issues.Microgrids appear to be the solution for large-scale renewable energy integration in these sectors.The microgrid components must be optimally planned and operated to prevent high costs, technical issues, and emissions. Existing approaches for optimal microgrid planning and operation in the literature do not include a solution for e-mobility infrastructure. As a consequence, a compact e-mobility infrastructure metho- dology is provided.The development of e-mobility infrastructure has as sociated uncertainties (short and long-term). As a result, a new stochastic method re- ferred to as IGDM-DRO is proposed in this dissertation.The proposed method provides a risk-averse strategy for microgrid planning and operation by including long-term and short-term uncertainty related to e-mobility.The multi-cut ben- der decomposition is applied for IGDM-DRO to prevent the suggested method’s intractability.Finally, the deterministic and stochastic methodologies are com bined in an ovelholistic approach for microgrid design and operation in terms of cost and robustness.The proposed method ist ested on a new settlement area in Magdeburg, Germany, under three different EV development scenarios (nega- tive, trend, andpositive).The share for the number of electric vehicles reached 31 percent of conventional vehicles by the end of the planned horizon. As a result, the microgrid’s overall cost has been increased by 2.3 to 2.9 percent per electric vehicle.Three public electric vehicle charging stations will be required in the investigated settlement are a intrend 2031.The investigated settlement area will require a total cost of 127,029 € in the trend scenario.To achieve full robustness against long-term uncertainties,the cost of the microgrid needs to be increased by 80 percent

Coordinated Smart Home Thermal and Energy Management System Using a Co-simulation Framework

The increasing demand for electricity especially during the peak hours threaten the grid reliability. Demand response (DR), changing the load pattern of the consumer in response to system conditions, can decrease energy consumption during periods of high wholesale market price and also maintain system reliability. Residential homes consume 38% of the total electric energy in the U.S., making them promising for DR participation. Consumers can be motivated to participate in DR programs by providing incentives (incentive-based DR), or by introducing a time-varying tariff for electricity consumption (price-based DR). A home energy management system (HEMS), an automated system which can alter the residential consumer’s energy consumption pattern based on the price of electricity or financial incentives, enables the consumers to participate in such DR programs. HEMS also should consider consumer comfort during the scheduling of the heating, ventilation, and air conditioning (HVAC) and other appliances. As internal heat gain of appliances and people have a significant effect in the HVAC energy consumption, an integrated HVAC and appliance scheduling are necessary to properly evaluate potential benefits of HEMS. This work presents the formulation of HEMS considering combined scheduling of HVAC and appliances in time-varying tariff. The HEMS also considers the consumer comfort for the HVAC and appliances while minimizing the total electricity cost. Similarly, the HEMS also considers the detailed building model in EnergyPlus, a building energy analysis tool, to evaluate the effectiveness of the HEMS. HEMS+, a communication interface to EnergyPlus, is designed to couple HEMS and EnergyPlus in this work. Furthermore, a co-simulation framework coupling EnergyPlus and GridLAB-D, a distribution system simulation tool, is developed. This framework enables incorporation of the controllers such as HEMS and aggregator, allowing controllers to be tested in detail in both building and power system domains. Lack of coordination among a large number of HEMS responding to same price signal results in peak more severe than the normal operating condition. This work presents an incentive-based hierarchical control framework for coordinating and controlling a large number of residential consumers’ thermostatically controlled loads (TCLs) such as HVAC and electric water heater (EWH). The potential market-level economic benefits of the residential demand reduction are also quantified