Optimal Distributed Power Generation Under Network-Load Constraints

In electrical power networks nowadays more and more customers are becoming power-producers, mainly because of the development of novel components for decentralized power generation (solar panels, small wind turbines and heat pumps). This gives rise to the question how many units of each type (solar panel, small wind turbine or central heating power units) can be inserted into any transmission line in the network, such that under given distributions on the typical production and consumption over time, the maximum loads on the lines and components will not be exceeded. In this paper, we present a linear programming model for maximizing the amount of decentralized power generation while respecting the load limitations of the network. We describe a prototype showing that for an example network the maximization problem can be solved efficiently. We also modeled the case were the power consumption and decentralized power generation are considered as stochastic variables, which is inherently more complex

Stochastic equilibrium models for generation capacity expansion

Capacity expansion models in the power sector were among the first applications of operations research to the industry. The models lost some of their appeal at the inception of restructuring even though they still offer a lot of possibilities and are in many respect irreplaceable provided they are adapted to the new environment. We introduce stochastic equilibrium versions of these models that we believe provide a relevant context for looking at the current very risky market where the power industry invests and operates. We then take up different questions raised by the new environment. Some are due to developments of the industry like demand side management: an optimization framework has difficulties accommodating them but the more general equilibrium paradigm offers additional possibilities. We then look at the insertion of risk related investment practices that developed with the new environment and may not be easy to accommodate in an optimization context. Specifically we consider the use of plant specific discount rates that we derive by including stochastic discount rates in the equilibrium model. Linear discount factors only price systematic risk. We therefore complete the discussion by inserting different risk functions (for different agents) in order to account for additional unpriced idiosyncratic risk in investments. These different models can be cast in a single mathematical representation but they do not have the same mathematical properties. We illustrate the impact of these phenomena on a small but realistic example.capacity adequacy, risk functions, stochastic equilibrium models, stochastic discount factors

Short-term Risk Management for Electricity Retailers Under Rising Shares of Decentralized Solar Generation

Electricity retailers face increasing uncertainty due to the ongoing expansion of unpredictable, distributed generation in the residential sector. We analyze how increasing levels of households\u27 solar PV self-generation affect the short-term decisionmaking and associated risk exposure of electricity retailers in day-ahead and intraday markets. First, we develop a stochastic model accounting for correlations between solar load, residual load and price in sequentially nested wholesale spot markets across seasons and type of day. Second, we develop a computationally tractable twostage stochastic mixed-integer optimization model to investigate the trading portfolio and risk optimization problem faced by retailers. Through conditional value-at-risk we assess retailers\u27 profitability and risk exposure to different levels of PV self-generation by assuming different retail tariff schemes. We find risk-hedging trading strategies and tariffs to have greater impact in Summer and with low levels of residual load in the system, i.e. when the solar generation uncertainty affect more the households demand to be served and the wholesale spot prices. The study is innovative in unveiling the potential of dynamic electricity tariffs, which are indexed to spot prices, to sustain a high penetration of renewable energy source while promoting risk sharing between customer and retailer. Our findings have implications for electricity retailers facing load and revenue risks in wholesale spot markets, likewise for regulators and policy-makers interested in electricity market design

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

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

Reliability standards for the operation and planning of future electricity networks

Electricity networks, designed and operated in accordance with the historic deterministic standards, have broadly delivered secure and reliable supplies to customers. A key issue regarding their evolution is how the operation and planning standards should evolve to make efficient use of the existing assets while taking advantage of emerging, non-network (or non-wires) technologies. Deployment of the smart grid will require fundamental changes in the historical principles used for network security in order to ensure that integration of low-carbon generation is undertaken as efficiently as possible through the use of new information and communication technology (ICT), and new flexible network technologies that can maximize utilization of existing electricity infrastructure. These new technologies could reduce network redundancy in providing security of supply by enabling the application of a range of advanced, technically effective, and economically efficient corrective (or post-fault) actions that can release latent network capacity of the existing system. In this context, this paper demonstrates that historical deterministic practices and standards, mostly developed in the 1950s, should be reviewed in order to take full advantage of new emerging technologies and facilitate transition to a smart grid paradigm. This paper also demonstrates that a probabilistic approach to developing future efficient operating and design strategies enabled by new technologies, will appropriately balance network investment against non-network solutions while truly recognizing effects of adverse weather, common-mode failures, high-impact low-probability events, changing market prices for pre- and post-contingency actions, equipment malfunctioning, etc. This clearly requires explicit consideration of the likelihood of various outages (beyond those considered in deterministic studies) and quantification of their impacts on alternative network operation and investment decisions, which cannot be undertaken in a deterministic, “one size fits all” framework. In this context, we developed advanced optimization models aimed at determining operational and design network decisions based on both deterministic and probabilistic security principles. The proposed models can recognize network constraints/congestion and various operational measures (enabled by new technologies) composed of preventive and corrective control actions such as operation of special protection schemes, demand side response and generation reserve utilization and commitment, considering potential outages of network and generation facilities. The probabilistic model proposed can also provide targeted levels of reliability and limit exposure to severe low probability events (mainly driven by natural hazards) through the use of Conditional Value at Risk (CVaR) constraints, delivering robust and resilient supplies to consumers at the minimum cost. Through various case studies conducted on the Great Britain (GB) power network, we set out the key questions that need to be addressed in support of the change in network reliability paradigm, provide an overview of the key modelling approaches proposed for assessing the risk profile of operation of future networks, propose a framework for a fundamental review of the existing network security standards, and set out challenges for assessing the reliability and economics of the operation of future electricity network

Decision Making under Uncertainty and Competition for Sustainable Energy Technologies

This dissertation addresses the main challenges faced in the transition to a more sustainable energy sector by applying modelling tools that could design more effective managerial responses and provide policy insights. To mitigate the impact of climate change, the electric power industry needs to reduce markedly its emissions of greenhouse gases. As energy consumption is set to increase in the foreseeable future, this can be achieved only through costly investments in more efficient conventional generation or in renewable energy resources. While more energy-efficient technologies are commercially available, the deregulation of most electricity industries implies that investment decisions need to be taken by private investors with government involvement limited to setting policy measures or designing market rules. Thus, it is desirable to understand how investment and operational decisions are to be made by decentralised entities that face uncertainty and competition. One of the most efficient thermal power technologies is cogeneration, or combined heat and power (CHP), which can recover heat that otherwise would be discarded from conventional generation. Cogeneration is particularly efficient when the recovered heat can be used in the vicinity of the combustion engine. Although governments are supporting on-site CHP generation through feed-in tariffs and favourable grid access, the adoption of small-scale electricity generation has been hindered by uncertain electricity and gas prices. While deterministic and real options studies have revealed distributed generation to be both economical and effective at reducing CO2 emissions, these analyses have not addressed the aspect of risk management. In order to overcome the barriers of financial uncertainties to investment, it is imperative to address the decision-making problems of a risk-averse energy consumer. Towards that end, we develop a multi-stage, stochastic mean-risk optimisation model for the long-term and medium-term risk management problems of a large consumer. We first show that installing a CHP unit not only results in both lower CO2 emissions and expected running cost but also leads to lower risk exposure. In essence, by investing in a CHP unit, a large consumer obtains the option to use on-site generation whenever the electricity price peaks, thereby reducing significantly its financial risk over the investment period. To provide further insights into risk management strategies with on-site generation, we examine also the medium-term operational problem of a large consumer. In this model, we include all available contracts from electricity and gas futures markets, and analyse their interactions with on-site generation. We conclude that by swapping the volatile electricity spot price for the less volatile gas spot price, on-site generation with CHP can lead to lower risk exposure even in the medium term, and it alters a risk-averse consumer’s demand for futures contracts. While extensive subsidies have triggered investments in renewable generation, these installations need to be accompanied by transmission expansion. The reason for this is that solar and wind energy output is intermittent, and attractive solar and wind sites are often located far away from demand centres. Thus, to integrate renewable generation into the grid system and to maintain a reliable and secure electricity supply, a vastly improved transmission network is crucial. Finding the optimal transmission line investments for a given network is already a very complex task since these decisions need to take into account future demand and generation configurations, too, which now depend on private investors. To address these concerns, our third study models the problem of wind energy investment and transmission expansion jointly through a stochastic bi-level programming model under different market designs for transmission line investment. This enables the game-theoretic interaction between distinct decision makers, i.e., those investing in power plants and those constructing transmission lines, to be addressed directly. We find that under perfect competition only one of the wind power producers, the one with lower capital cost, makes investment and to a lower degree under a profit-maximising merchant investor (MI) than under a welfare-maximising transmission system operator (TSO), as the MI reduces the transmission capacity to increase congestion rent. In addition, we note that regardless of whether the grid expansion is carried out by the TSO or by the MI, a higher proportion of wind energy is installed when power producers exercise market power. In effect, strategic withholding of generation capacity by producers prompts more transmission investment since the TSO aims to increase welfare by subsidising wind and the MI creates more flow to maximise profit. Under perfect competition, a higher level of wind generation can be achieved only through mandating renewable portfolio standards (RPS), which in turn results also in increased transmission investment

Reserve services provision by demand side resources in systems with high renewables penetration using stochastic optimization

It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique.It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique

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

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