Grid congestion mitigation and battery degradation minimisation using model predictive control in PV-based microgrid

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksIncreasing integration of photovoltaic (PV) system in electric grids cause congestion during peak power feed-in. Battery storage in PV systems increases self-consumption, for consumer's benefit. However with conventional maximising self consumption (MSC) control for battery scheduling, the issue of grid congestion is not addressed. The batteries tend to be fully charged early in the day and peak power is still fed-in to grid. This also increases battery degradation due to increased dwell time at high state of charge (SOC) levels. To address this issue, this work uses a model predictive control (MPC) for scheduling in PV system with battery storage to achieve multiple objectives of minimising battery degradation, grid congestion, while maximising self consumption. In order to demonstrate the improvement, this work compares the performances of MPC and MSC schemes when used in battery scheduling. The improvement is quantified through performance indices like self consumption ratio, peak power reduction and battery capacity fade for one-year operation. An analysis on computation burden and maximum deterioration in MPC performance under prediction error is also carried out. It is concluded that, compared to MSC, MPC achieves similar self consumption in PV systems while also reducing grid congestion and battery degradation.Peer ReviewedPostprint (author's final draft

Resilience-driven planning and operation of networked microgrids featuring decentralisation and flexibility

High-impact and low-probability extreme events including both man-made events and natural weather events can cause severe damage to power systems. These events are typically rare but featured in long duration and large scale. Many research efforts have been conducted on the resilience enhancement of modern power systems. In recent years, microgrids (MGs) with distributed energy resources (DERs) including both conventional generation resources and renewable energy sources provide a viable solution for the resilience enhancement of such multi-energy systems during extreme events. More specifically, several islanded MGs after extreme events can be connected with each other as a cluster, which has the advantage of significantly reducing load shedding through energy sharing among them. On the other hand, mobile power sources (MPSs) such as mobile energy storage systems (MESSs), electric vehicles (EVs), and mobile emergency generators (MEGs) have been gradually deployed in current energy systems for resilience enhancement due to their significant advantages on mobility and flexibility. Given such a context, a literature review on resilience-driven planning and operation problems featuring MGs is presented in detail, while research limitations are summarised briefly. Then, this thesis investigates how to develop appropriate planning and operation models for the resilience enhancement of networked MGs via different types of DERs (e.g., MGs, ESSs, EVs, MESSs, etc.). This research is conducted in the following application scenarios: 1. This thesis proposes novel operation strategies for hybrid AC/DC MGs and networked MGs towards resilience enhancement. Three modelling approaches including centralised control, hierarchical control, and distributed control have been applied to formulate the proposed operation problems. A detailed non-linear AC OPF algorithm is employed to model each MG capturing all the network and technical constraints relating to stability properties (e.g., voltage limits, active and reactive power flow limits, and power losses), while uncertainties associated with renewable energy sources and load profiles are incorporated into the proposed models via stochastic programming. Impacts of limited generation resources, load distinction intro critical and non-critical, and severe contingencies (e.g., multiple line outages) are appropriately captured to mimic a realistic scenario. 2. This thesis introduces MPSs (e.g., EVs and MESSs) into the suggested networked MGs against the severe contingencies caused by extreme events. Specifically, time-coupled routing and scheduling characteristics of MPSs inside each MG are modelled to reduce load shedding when large damage is caused to each MG during extreme events. Both transportation networks and power networks are considered in the proposed models, while transporting time of MPSs between different transportation nodes is also appropriately captured. 3. This thesis focuses on developing realistic planning models for the optimal sizing problem of networked MGs capturing a trade-off between resilience and cost, while both internal uncertainties and external contingencies are considered in the suggested three-level planning model. Additionally, a resilience-driven planning model is developed to solve the coupled optimal sizing and pre-positioning problem of MESSs in the context of decentralised networked MGs. Internal uncertainties are captured in the model via stochastic programming, while external contingencies are included through the three-level structure. 4. This thesis investigates the application of artificial intelligence techniques to power system operations. Specifically, a model-free multi-agent reinforcement learning (MARL) approach is proposed for the coordinated routing and scheduling problem of multiple MESSs towards resilience enhancement. The parameterized double deep Q-network method (P-DDQN) is employed to capture a hybrid policy including both discrete and continuous actions. A coupled power-transportation network featuring a linearised AC OPF algorithm is realised as the environment, while uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through the learning procedure.Open Acces

Distribution energy storage investment prioritization with a real coded multi-objective genetic algorithm

Energy Storage Systems (ESSs) are progressively becoming an essential requisite for the upcoming Smart Distribution Systems thanks to the flexibility they introduce in the network operation. A rapid improvement in ESS technology efficiency has been seen, but not yet sufficient to drastically reduce the high investments associated. Thus, optimal planning and management of these devices are crucial to identify specific configurations that can justify ESSs installation. This consideration has motivated a strong interest of the researchers in this field that, however, have separately solved the optimal ESS location and the optimal ESS schedule. In the paper, a novel multi-objective approach is presented, based on the Non-dominated Sorted Genetic Algorithm - II integrated with a real codification that allows joining in a single optimization all the main features of an optimal ESS implementation project: siting, sizing and scheduling. The methodology has been tested on a real-size rural distribution network

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

Multi-objective robust optimization to solve energy scheduling in buildings under uncertainty

With the high penetration of renewable generation in Smart Grids (SG), the uncertainty behavior associated with the forecast of weather conditions possesses a new degree of complexity in the Energy Resource Management (ERM) problem. In this paper, a Multi-Objective Particle Swarm Optimization (MOPSO) methodology is proposed to solve ERM problem in buildings with penetration of Distributed Generation (DG) and Electric Vehicles (EVs) and considering the uncertainty of photovoltaic (PV) generation. The proposed methodology aims to maximize profits while minimizing CO 2 emissions. The uncertainty of PV generation is modeled with the use of Monte Carlo simulation in the evaluation process of the MOPSO core. Also, a robust optimization approach is adopted to select the best solution for the worst-case scenario of PV generation. A case study is presented using a real building facility from Brazil, to verify the effectiveness of the implemented robust MOPSO.This work has received funding from the Project NetEffiCity (ANI|P2020 18015), and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013.info:eu-repo/semantics/publishedVersio

Recent techniques used in home energy management systems: a review

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.info:eu-repo/semantics/publishedVersio