Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea

Distributed demand-side optimization in the smart grid

The modern power grid is facing major challenges in the transition to a low-carbon energy sector. The growing energy demand and environmental concerns require carefully revisiting how electricity is generated, transmitted, and consumed, with an eye to the integration of renewable energy sources. The envisioned smart grid is expected to address such issues by introducing advanced information, control, and communication technologies into the energy infrastructure. In this context, demand-side management (DSM) makes the end users responsible for improving the efficiency, reliability and sustainability of the power system: this opens up unprecedented possibilities for optimizing the energy usage and cost at different levels of the network. The design of DSM techniques has been extensively discussed in the literature in the last decade, although the performance of these methods has been scarcely investigated from the analytical point of view. In this thesis, we consider the demand-side of the electrical network as a multiuser system composed of coupled active consumers with DSM capabilities and we propose a general framework for analyzing and solving demand-side management problems. Since centralized solution methods are too demanding in most practical applications due to their inherent computational complexity and communication overhead, we focus on developing efficient distributed algorithms, with particular emphasis on crucial issues such as convergence speed, information exchange, scalability, and privacy. In this respect, we provide a rigorous theoretical analysis of the conditions ensuring the existence of optimal solutions and the convergence of the proposed algorithms. Among the plethora of DSM methods, energy consumption scheduling (ECS) programs allow to modify the user's demand profile by rescheduling flexible loads to off-peak hours. On the other hand, incorporating dispatchable distributed generation (DG) and distributed storage (DS) into the demand-side of the network has been shown to be equally successful in diminishing the peak-to-average ratio of the demand curve, plus overcoming the limitations in terms users' inconvenience introduced by ECS. Quite surprisingly, while the literature has mostly concentrated on ECS techniques, DSM approaches based on dispatchable DG and DS have not attracted the deserved attention despite their load-shaping potential and their capacity to facilitate the integration of renewable sources. In this dissertation, we fill this gap and devise accurate DSM models to study the impact of dispatchable DG and DS at the level of the end users and on the whole electricity infrastructure. With this objective in mind, we tackle several DSM scenarios, starting from a deterministic day-ahead optimization with local constraints and culminating with a stochastic day-ahead optimization combined with real-time adjustments under both local and global requirements. Each task is complemented by defining appropriate network and pricing models that enable the implementation of the DSM paradigm in realistic energy market environments. In this regard, we design both user-oriented and holistic-based DSM optimization frameworks, which are respectively applicable to competitive and externally regulated market scenarios. Numerical results are reported to corroborate the presented distributed schemes. On the one hand, the users' electricity expenditures are consistently reduced, which encourages their active and voluntary participation in the proposed DSM programs; on the other hand, this results in a lower generation costs and enhances the robustness of the whole grid.La xarxa elèctrica moderna s'enfronta a enormes reptes en la transició cap a un sector energètic de baixa generació de carboni. La creixent demanda d'energia i les preocupacions ambientals requereixen revisar acuradament com es genera, transmet, i consumeix l'electricitat, amb l'objectiu de la integració de les fonts d'energia renovables. S'espera que el concepte de smart grid pugui abordar aquestes qüestions mitjançant la introducció d’informació avançada, control i tecnologies de la comunicació en la infraestructura energètica. En aquest context, el concepte de gestió de la demanda (DSM) fa que els usuaris finals siguin responsables de millorar l’eficiència, la fiabilitat i la sostenibilitat del sistema de potència obrint possibilitats sense precedents per a l’optimització de l’ús i el cost de l'energia en els diferents nivells de la xarxa. El disseny de tècniques de DSM s'ha debatut àmpliament en la literatura durant l’ultima dècada, tot i que el rendiment d'aquests mètodes ha estat poc investigat des del punt de vista analític. En aquesta tesi es considera la demanda de la xarxa elèctrica com un sistema multiusuari format per consumidors actius amb capacitats de DSM i es proposa un marc general per analitzar i resoldre problemes de gestió. Donat que els mètodes de solució centralitzats són excessivament exigents per a aplicacions pràctiques per la seva complexitat computacional i al inherent sobrecost de comunicació, ens centrem en el desenvolupament d'algorismes distribuïts, amb especial èmfasi en temes crucials com la velocitat de convergència, l'intercanvi d’informació, l'escalabilitat i la privacitat. En aquest sentit, oferim un rigorós anàlisi teòric de les condicions que garanteixen l’existència de solucions òptimes i la convergència dels algorismes proposats. Entre la gran quantitat de mètodes de DSM, els programes de programació del consum d'energia (ECS) permeten modificar el perfil de la demanda dels usuaris a través de la reprogramació de càrregues flexibles durant hores de baix consum. D'altra banda, la incorporació de generació distribuïda (DG) i d'emmagatzematge distribuït (DS) ha demostrat ser igualment eficaç disminuint la relació entre potència de pic i mitja de la corba de demanda, evitant els inconvenients introduïts pel ECS als usuaris. Sorprenentment, si bé que la literatura s'ha concentrat sobretot en les tècniques de ECS, les tècniques de DSM basades en DG i DS no han atret l’atenció merescuda malgrat el seu potencial de confirmació de la càrrega i la seva capacitat de facilitar la integració de les fonts renovables. En aquesta tesi, omplim aquest buit i elaborem models precisos de DSM per estudiar l'impacte de DG i DS a nivell dels usuaris finals i de tota la infraestructura elèctrica . Tenint present aquest objectiu, fem front a diversos escenaris de DSM, partint d'una optimització sobre les previsions amb un dia d’antelació (day-ahead). Es considera des del cas determinista amb restriccions locals fins al cas estocàstic combinat amb ajustos en temps real i amb restriccions locals i globals. Cada tasca es complementa amb la definició de models de xarxa i de tarifació apropiats que permetin la posada en pràctica del paradigma de DSM en entorns realistes del mercat energètic. En aquest sentit vam dissenyar marcs d’optimització de DSM globals i orientats als usuaris, que són respectivament aplicables a situacions de mercat competitives i regulades externament. Els resultats numèrics reportats corroboren els esquemes distribuïts presentats. D'una banda, les despeses d'electricitat dels usuaris es redueixen de forma consistent, el que fomenta la seva participació activa en els programes de DSM proposats; per una altra banda, aquesta optimització resulta en un cost de generació inferior i millora la robustesa de tota la xarxa.La red eléctrica moderna se enfrenta a enormes retos en la transición hacia un sector energético de baja generación de carbono. La creciente demanda de energía y las preocupaciones ambientales requieren revisar cuidadosamente cómo se genera, transmite y consume la electricidad, con vista a la integración de las fuentes de energía renovable. Se espera que el concepto de smart grid pueda abordar estas cuestiones mediante la introducción de información avanzada, control y tecnologías de la comunicación en la infraestructura energética. En este contexto, el concepto de gestión de la demanda (DSM) hace que los usuarios finales sean responsables de mejorar la eficiencia, la fiabilidad y la sostenibilidad del sistema de potencia abriéndose posibilidades sin precedentes para la optimización del uso y el coste de la energía en los diferentes niveles de la red. El diseño de técnicas de DSM se ha debatido ampliamente en la literatura en la última década, aunque el rendimiento de estos métodos ha sido poco investigado desde el punto de vista analítico. En esta tesis se considera la demanda de la red eléctrica como un sistema multiusuario compuesto por consumidores activos con capacidades de DSM y se propone un marco general para analizar y resolver problemas de gestión de demanda. Dado que los métodos de solución centralizados son excesivamente exigentes para aplicaciones prácticas debido a su complejidad computacional y al inherente sobrecoste de comunicación, nos centramos en el desarrollo de algoritmos distribuidos, con especial énfasis en temas cruciales como la velocidad de convergencia, el intercambio de información, la escalabilidad y la privacidad. En este sentido, ofrecemos un riguroso análisis teórico de las condiciones que garantizan la existencia de soluciones óptimas y la convergencia de los algoritmos propuestos. Entre la gran cantidad de métodos de DSM, los programas de programación del consumo de energía (ECS) permiten modificar el perfil de la demanda de los usuarios a través de la reprogramación de cargas flexibles durante horas de bajo consumo. Por otro lado, la incorporación de generación distribuida (DG) y de almacenamiento distribuido (DS) ha demostrado ser igualmente eficaz disminuyendo la relación entre potencia de pico y media de la curva de demanda, evitando los inconvenientes introducidos por el ECS a los usuarios. Sorprendentemente, mientras que la literatura se ha concentrado sobre todo en las técnicas de ECS, los programas de DSM basados en DG y DS no han atraído la atención merecida a pesar de su potencial de conformación de la carga y su capacidad de facilitar la integración de las fuentes renovables. En esta tesis, llenamos este vacío y elaboramos modelos precisos de DSM para estudiar el impacto de DG y DS a nivel de los usuarios finales y de toda la infraestructura eléctrica. Teniendo presente este objetivo, hacemos frente a varios escenarios de DSM, a partir de una optimización sobre las previsiones con un día de antelación (day-ahead). Se considera desde el caso determinista con restricciones locales hasta el caso estocástico combinado con ajustes en tiempo real y con restricciones locales y globales. Cada tarea se complementa con la definición de modelos de red y de tarificación apropiados que permitan la puesta en práctica del paradigma de DSM en entornos realistas del mercado energético. En este sentido diseñamos marcos de optimización de DSM globales y orientados a los usuarios, que son respectivamente aplicables a situaciones de mercado competitivas y reguladas externamente. Los resultados numéricos reportados corroboran los esquemas distribuidos presentados. Por un lado, los gastos de electricidad de los usuarios se reducen de forma consistente, lo que fomenta su participación activa en los programas de DSM propuestos; por otra parte, esta optimización resulta en un coste de generación inferior y mejora la robustez de toda la re

Building and investigating generators' bidding strategies in an electricity market

In a deregulated electricity market environment, Generation Companies (GENCOs) compete with each other in the market through spot energy trading, bilateral contracts and other financial instruments. For a GENCO, risk management is among the most important tasks. At the same time, how to maximise its profit in the electricity market is the primary objective of its operations and strategic planning. Therefore, to achieve the best risk-return trade-off, a GENCO needs to determine how to allocate its assets. This problem is also called portfolio optimization. This dissertation presents advanced techniques for generator strategic bidding, portfolio optimization, risk assessment, and a framework for system adequacy optimisation and control in an electricity market environment. Most of the generator bidding related problems can be regarded as complex optimisation problems. In this dissertation, detailed discussions of optimisation methods are given and a number of approaches are proposed based on heuristic global optimisation algorithms for optimisation purposes. The increased level of uncertainty in an electricity market can result in higher risk for market participants, especially GENCOs, and contribute significantly to the drivers for appropriate bidding and risk management tasks for GENCOs in the market. Accordingly, how to build an optimal bidding strategy considering market uncertainty is a fundamental task for GENCOs. A framework of optimal bidding strategy is developed out of this research. To further enhance the effectiveness of the optimal bidding framework; a Support Vector Machine (SVM) based method is developed to handle the incomplete information of other generators in the market, and therefore form a reliable basis for a particular GENCO to build an optimal bidding strategy. A portfolio optimisation model is proposed to maximise the return and minimise the risk of a GENCO by optimally allocating the GENCO's assets among different markets, namely spot market and financial market. A new market pnce forecasting framework is given In this dissertation as an indispensable part of the overall research topic. It further enhances the bidding and portfolio selection methods by providing more reliable market price information and therefore concludes a rather comprehensive package for GENCO risk management in a market environment. A detailed risk assessment method is presented to further the price modelling work and cover the associated risk management practices in an electricity market. In addition to the issues stemmed from the individual GENCO, issues from an electricity market should also be considered in order to draw a whole picture of a GENCO's risk management. In summary, the contributions of this thesis include: 1) a framework of GENCO strategic bidding considering market uncertainty and incomplete information from rivals; 2) a portfolio optimisation model achieving best risk-return trade-off; 3) a FIA based MCP forecasting method; and 4) a risk assessment method and portfolio evaluation framework quantifying market risk exposure; through out the research, real market data and structure from the Australian NEM are used to validate the methods. This research has led to a number of publications in book chapters, journals and refereed conference proceedings