Quantum Particle Swarm Optimization Applied to Distinct Remuneration Approaches in Demand Response Programs

The development of demand response programs has been allowing to improve power system performance in several ways, both in terms of the management of electricity markets, as well as regarding benefits in its operation. In order to model the remuneration for the participation of consumers in the scheduling of resources, this paper proposes a methodology based on the use of four incentive-based tariffs for the remuneration of demand response participation. It considers steps, quadratic, constant and linear remuneration. The optimization model enables Virtual Power Players to minimize operation costs, considering different critical situations of management and operation. The optimization problem has been solved by Quantum Particle Swarm Optimization. The case study concerns 168 consumers, classified into 5 consumer types, 118 distributed generation resources and 4 external suppliers.The present work was done and funded in the scope of the following projects: EUREKA - ITEA2 Project SEAS with project number 12004; ELECON Project, REA grant agreement No 318912 (FP7 PIRSES-GA2012-318912); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

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

Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities

[EN] Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting timeUllah, B.; Hussain, I.; Uthansakul, P.; Riaz, M.; Khan, MN.; Lloret, J. (2020). Exploiting multi-verse optimization and sine-cosine algorithms for energy management in smart cities. Applied Sciences. 10(6):1-21. https://doi.org/10.3390/app1006209512110

Artificial intelligence for decision making in energy demand-side response

This thesis examines the role and application of data-driven Artificial Intelligence (AI) approaches for the energy demand-side response (DR). It follows the point of view of a service provider company/aggregator looking to support its decision-making and operation. Overall, the study identifies data-driven AI methods as an essential tool and a key enabler for DR. The thesis is organised into two parts. It first provides an overview of AI methods utilised for DR applications based on a systematic review of over 160 papers, 40 commercial initiatives, and 21 large-scale projects. The reviewed work is categorised based on the type of AI algorithm(s) employed and the DR application area of the AI methods. The end of the first part of the thesis discusses the advantages and potential limitations of the reviewed AI techniques for different DR tasks and how they compare to traditional approaches. The second part of the thesis centres around designing machine learning algorithms for DR. The undertaken empirical work highlights the importance of data quality for providing fair, robust, and safe AI systems in DR — a high-stakes domain. It furthers the state of the art by providing a structured approach for data preparation and data augmentation in DR to minimise propagating effects in the modelling process. The empirical findings on residential response behaviour show better response behaviour in households with internet access, air-conditioning systems, power-intensive appliances, and lower gas usage. However, some insights raise questions about whether the reported levels of consumers’ engagement in DR schemes translate to actual curtailment behaviour and the individual rationale of customer response to DR signals. The presented approach also proposes a reinforcement learning framework for the decision problem of an aggregator selecting a set of consumers for DR events. This approach can support an aggregator in leveraging small-scale flexibility resources by providing an automated end-to-end framework to select the set of consumers for demand curtailment during Demand-Side Response (DR) signals in a dynamic environment while considering a long-term view of their selection process

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Optimization of Aggregators Energy Resources considering Local Markets and Electric Vehicle Penetration

O sector elétrico tem vindo a evoluir ao longo do tempo. Esta situação deve-se ao facto de surgirem novas metodologias para lidarem com a elevada penetração dos recursos energéticos distribuídos (RED), principalmente veículos elétricos (VEs). Neste caso, a gestão dos recursos energéticos tornou-se mais proeminente devido aos avanços tecnológicos que estão a ocorrer, principalmente no contexto das redes inteligentes. Este facto torna-se importante, devido à incerteza decorrente deste tipo de recursos. Para resolver problemas que envolvem variabilidade, os métodos baseados na inteligência computacional estão a se tornar os mais adequados devido à sua fácil implementação e baixo esforço computacional, mais precisamente para o caso tratado na tese, algoritmos de computação evolucionária (CE). Este tipo de algoritmo tenta imitar o comportamento observado na natureza. Ao contrário dos métodos determinísticos, a CEé tolerante à incerteza; ou seja, é adequado para resolver problemas relacionados com os sistemas energéticos. Estes sistemas são geralmente de grandes dimensões, com um número crescente de variáveis e restrições. Aqui a IC permite obter uma solução quase ótima em tempo computacional aceitável com baixos requisitos de memória. O principal objetivo deste trabalho foi propor um modelo para a programação dos recursos energéticos dos recursos dedicados para o contexto intradiário, para a hora seguinte, partindo inicialmente da programação feita para o dia seguinte, ou seja, 24 horas para o dia seguinte. Esta programação é feita por cada agregador (no total cinco) através de meta-heurísticas, com o objetivo de minimizar os custos ou maximizar os lucros. Estes agregadores estão inseridos numa cidade inteligente com uma rede de distribuição de 13 barramentos com elevada penetração de RED, principalmente energia renovável e VEs (2000 VEs são considerados nas simulações). Para modelar a incerteza associada ao RED e aos preços de mercado, vários cenários são gerados através da simulação de Monte Carlo usando as funções de distribuição de probabilidade de erros de previsão, neste caso a função de distribuição normal para o dia seguinte. No que toca à incerteza no modelo para a hora seguinte, múltiplos cenários são gerados a partir do cenário com maior probabilidade do dia seguinte. Neste trabalho, os mercados locais de eletricidade são também utilizados como estratégia para satisfazer a equação do balanço energético onde os agregadores vão para vender o excesso de energia ou comprar para satisfazer o consumo. Múltiplas metaheurísticas de última geração são usadas para fazer este escalonamento, nomeadamente Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with Normal-Cauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Os resultados mostram que o modelo proposto é eficaz para os múltiplos agregadores com variações de custo na sua maioria abaixo dos 5% em relação ao dia seguinte, exceto para o agregador e de VEs. É também aplicado um teste Wilcoxon para comparar o desempenho do algoritmo CUMDANCauchy++ com as restantes meta-heurísticas. O CUMDANCauchy++ mostra resultados competitivos tendo melhor performance que todos os algoritmos para todos os agregadores exceto o DEEDA que apresenta resultados semelhantes. Uma estratégia de aversão ao risco é implementada para um agregador no contexto do dia seguinte para se obter uma solução mais segura e robusta. Os resultados mostram um aumento de quase 4% no investimento, mas uma redução de até 14% para o custo dos piores cenários.The electrical sector has been evolving. This situation is because new methodologies emerge to deal with the high penetration of distributed energy resources (DER), mainly electric vehicles (EVs). In this case, energy resource management has become increasingly prominent due to the technological advances that are taking place, mainly in the context of smart grids. This factor becomes essential due to the uncertainty of this type of resource. To solve problems involving variability, methods based on computational intelligence (CI) are becoming the most suitable because of their easy implementation and low computational effort, more precisely for the case treated in this thesis, evolutionary computation (EC) algorithms. This type of algorithm tries to mimic behavior observed in nature. Unlike deterministic methods, the EC is tolerant of uncertainty, and thus it is suitable for solving problems related to energy systems. These systems are usually of high dimensions, with an increased number of variables and restrictions. Here the CI allows obtaining a near-optimal solution in good computational time with low memory requirements. This work's main objective is to propose a model for the energy resource scheduling of the dedicated resources for the intraday context, for the our-ahead, starting initially from the scheduling done for the day ahead, that is, 24 hours for the next day. This scheduling is done by each aggregator (in total five) through metaheuristics to minimize the costs or maximize the profits. These aggregators are inserted in a smart city with a distribution network of 13 buses with a high penetration of DER, mainly renewable energy and EVs (2000 EVs are considered in the simulations). Several scenarios are generated through Monte Carlo Simulation using the forecast errors' probability distribution functions, the normal distribution function for the day-ahead to model the uncertainty associated with DER and market prices. Multiple scenarios are developed through the highest probability scenario from the day-ahead when it comes to intraday uncertainty. In this work, local electricity markets are used as a mechanism to satisfy the energy balance equation where each aggregator can sell the excess of energy or buy more to meet the demand. Several recent and modern metaheuristics are used to solve the proposed problems in the thesis, namely Differential Evolution (DE), Hybrid-Adaptive DE with Decay function (HyDE-DF), DE with Estimation of Distribution Algorithm (DEEDA), Cellular Univariate Marginal Distribution Algorithm with NormalCauchy Distribution (CUMDANCauchy++), Hill Climbing to Ring Cellular Encode-Decode UMDA (HC2RCEDUMDA). Results show that the proposed model is effective for the multiple aggregators. The metaheuristics present satisfactory results and mostly less than 5% variation in costs from the day-ahead except for the EV aggregator. A Wilcoxon test is also applied to compare the performance of the CUMDANCauchy++ algorithm with the remaining metaheuristics. CUMDANCauchy++ shows competitive results beating all algorithms in all aggregators except for DEEDA, which presents similar results. A risk aversion strategy is implemented for an aggregator in the day-ahead context to get a safer and more robust solution. Results show an increase of nearly 4% in day-ahead cost but a reduction of up to 14% of worst scenario cost

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid

Mathematical framework for designing energy matching and trading within green building neighbourhood system

Nowadays, energy efficiency, energy matching and trading, power production based on renewable energyresources, improving reliability, increasing power quality and other concepts are providing the most important topics in the power systems analysis especially in green building in the neighbourhood systems (GBNS). To do so, the need to obtain the optimal and economical dispatch of energy matching and trading should be expressed at the same time. Although, there are some solutions in literature but there is still a lack of mathematical framework for energy matching and trading in GBNS. In this dissertation, a mathematical framework is developed with the aim of supporting an optimal energy matching and trading within a GBNS.This aim will be achieved through several optimization algorithms based on heuristic and realistic optimization techniques. The appearance of new methods based on optimization algorithms and the challenges of managing a system contain different type of energy resources was also replicating the challenges encountered in this thesis. As a result, these methods are needed to be applied in such a way to achieve maximum efficiency,enhance the economic dispatch as well as to provide the best performance in GBNS. In order to validate theproposed framework, several case studies are simulated in this thesis and optimized based on various optimization algorithms. The better performances of the proposed algorithms are shown in comparison with the realistic optimization algorithms, and its effectiveness is validated over several GBs. The obtained results show convergence speed increase and the remarkable improvement of efficiency and accuracy under different condition. The obtained results clearly show that the proposed framework is effective in achieving optimal dispatch of generation resources in systems with multiple GBs and minimizing the market clearing price for the consumers and providing the better utilization of renewable energy sources