Impact of Forecast Errors on Expansion Planning of Power Systems with a Renewables Target

This paper analyzes the impact of production forecast errors on the expansion planning of a power system and investigates the influence of market design to facilitate the integration of renewable generation. For this purpose, we propose a stochastic programming modeling framework to determine the expansion plan that minimizes system-wide investment and operating costs, while ensuring a given share of renewable generation in the electricity supply. Unlike existing ones, this framework includes both a day-ahead and a balancing market so as to capture the impact of both production forecasts and the associated prediction errors. Within this framework, we consider two paradigmatic market designs that essentially differ in whether the day-ahead generation schedule and the subsequent balancing re-dispatch are co-optimized or not. The main features and results of the model set-ups are discussed using an illustrative four-node example and a more realistic 24-node case study

Adaptation pathways to reconcile hydropower generation and aquatic ecosystems restoration

The growing demands for water, food and energy, in addition to the need to protect ecosystems, pose significant challenges to water management and the operation of water systems. In hydropower-dominated basins, where reservoirs capture flow variability for energy generation, the modification of the natural flow regime disrupts the natural equilibrium of aquatic ecosystems. Migratory fish species and the associated ecosystem services are particularly vulnerable as the migration and recruitment success relies on the synchronization between the hydrologic flow regime and the reproductive cycle. While there is a consensus on the importance of restoring impacted ecosystems in balance with multiple uses, the current water governance framework lacks a comprehensive understanding of the tradeoffs involved and mechanisms for ensuring the equitable distribution of the adaptation costs among users. The present study brings a contribution to the field by proposing solutions to improve the water governance of river basins, combining the (1) identification of flow-ecological relationships by measuring the response of multiple options of flow regime restoration with a clear ecosystem indicator, (2) incorporation of the flow-ecological relationships and hydroclimatic conditions into the operation decisions of hydropower systems to create dynamic environmental flow solutions (termed Dynamic Adaptive Environmental flows – DAE-flows) with better long-term performance, (3) calculation of the reoperation trade-offs between alternative levels of environmental flow regime restoration and (4) development of mechanisms to share the adaptation costs among stakeholders. The electricity market is proposed as an institutional arrangement and financing mechanism to support the restoration of flow regimes in environmentally sensitive areas. The Upper Paraná River Basin, in Brazil, where consecutive hydropower impoundments have reduced the original floodplain along the last decades, is a recurrent example where reservoirs’ operation need to be reconciled with ecosystem functionality, which makes the basin an important study area. The findings of this dissertation indicate that it is possible to enhance the capacity of water systems to incorporate historically suppressed environmental water demands without imposing a hard constraint to economic uses. The consideration of the long-term effects of operation when designing operating strategies for multiple users leads to improved performance in both hydropower generation and meeting ecosystem demands. So, during severe droughts the water can still be reallocated to hydropower (as it is currently done) but at a lesser cost to the environment.As demandas crescentes por água, alimentos e energia, além da necessidade de proteger os ecossistemas, tornam a gestão dos recursos hídricos, bem como a operação de sistemas hídricos, uma tarefa desafiadora. Em bacias com aproveitamento hidrelétrico, a modificação do regime de vazões decorrente da operação dos reservatórios altera o equilíbrio natural dos ecossistemas aquáticos. Espécies migratórias de peixes e serviços ecossistêmicos associados ficam particularmente vulneráveis, uma vez que o sucesso da migração e recrutamento depende da sincronização entre o regime de vazão e o ciclo reprodutivo. Embora haja consenso sobre a importância de restaurar as demandas ecossistêmicas suprimidas e alcançar um equilíbrio que permita múltiplos usos, o atual quadro de governança carece de uma compreensão abrangente dos trade-offs envolvidos e dos mecanismos para garantir a distribuição equitativa dos custos de adaptação entre os usuários. O presente estudo contribui para o campo, propondo soluções para aprimorar a governança de bacias antropizadas, combinando (1) a identificação das relações vazão-ecológicas por meio da quantificação da resposta de múltiplas opções de restauração do regime de vazão por meio de um indicador de desempenho do ecossistema, (2) a incorporação dessas relações vazão-ecológicas juntamente com condições hidroclimáticas nas decisões operacionais de sistemas hidrelétricos (denominadas Vazões Ambientais Dinâmicas e Adaptativas - DAE-flows) para criar soluções dinâmicas de operação de reservatórios, (3) o cálculo dos trade-offs de reoperação de múltiplos níveis de restauração de regime de vazão ambiental e (4) o desenvolvimento de mecanismos para compartilhar os custos relacionados entre as partes interessadas. Nesse sentido, o mercado de eletricidade é proposto como arranjo institucional e mecanismo de financiamento para apoiar a restauração de regimes de vazão em áreas ambientalmente sensíveis. A Bacia Hidrográfica do Alto Paraná, Brasil, caracterizada como uma das mais represadas da América do Sul, com 65 usinas hidrelétricas integradas ao Sistema Integrado Nacional, é um exemplo recorrente da necessidade de reconciliação entre a geração de energia e a conservação de serviços ecossistêmicos, sendo utilizada como área de estudo. Os resultados indicam que podemos aumentar a capacidade dos sistemas hídricos para incorporar demandas ambientais historicamente suprimidas sem impor uma restrição rígida aos usos econômicos. Ao considerar os efeitos de longo prazo da operação ao projetar estratégias de operação para múltiplos usuários, obtemos um desempenho aprimorado tanto na geração de energia hidrelétrica quanto no atendimento às demandas do ecossistema. Assim, durante períodos de seca severa, a água ainda pode ser realocada para a produção de energia hidrelétrica (como é feito atualmente), porém com menor impacto ambiental

Multistage Stochastic Portfolio Optimisation in Deregulated Electricity Markets Using Linear Decision Rules

The deregulation of electricity markets increases the financial risk faced by retailers who procure electric energy on the spot market to meet their customers’ electricity demand. To hedge against this exposure, retailers often hold a portfolio of electricity derivative contracts. In this paper, we propose a multistage stochastic mean-variance optimisation model for the management of such a portfolio. To reduce computational complexity, we perform two approximations: stage-aggregation and linear decision rules (LDR). The LDR approach consists of restricting the set of decision rules to those affine in the history of the random parameters. When applied to mean-variance optimisation models, it leads to convex quadratic programs. Since their size grows typically only polynomially with the number of periods, they can be efficiently solved. Our numerical experiments illustrate the value of adaptivity inherent in the LDR method and its potential for enabling scalability to problems with many periods.OR in energy, electricity portfolio management, stochastic programming, risk management, linear decision rules

Water-related modelling in electric power systems: WATERFLEX Exploratory Research Project: version 1

Water is needed for energy. For instance, hydropower is the technology that generates more electricity worldwide after the fossil-fuelled power plants and its production depends on water availability and variability. Additionally, thermal power plants need water for cooling and thus generate electricity. On the other hand, energy is also needed for water. Given the increase of additional hydropower potential worldwide in the coming years, the high dependence of electricity generation with fossil-fuelled power plants, and the implications of the climate change, relevant international organisations have paid attention to the water-energy nexus (or more explicitly within a power system context, the water-power nexus). The Joint Research Centre of the European Commission, the United States Department of Energy, the Institute for Advanced Sustainability Studies, the Midwest Energy Research Consortium and the Water Council, or the Organisation for Economic Co-operation and Development, among others, have raised awareness about this nexus and its analysis as an integrated system. In order to properly analyse such linkages between the power and water sectors, there is a need for appropriate modelling frameworks and mathematical approaches. This report comprises the water-constrained models in electric power systems developed within the WATERFLEX Exploratory Research Project of the European Commission’s Joint Research Centre in order to analyse the water-power interactions. All these models are deemed modules of the Dispa-SET modelling tool. The version 1 of the medium-term hydrothermal coordination module is presented with some modelling extensions, namely the incorporation of transmission network constraints, water demands, and ecological flows. Another salient feature of this version of Dispa-SET is the modelling of the stochastic medium-term hydrothermal coordination problem. The stochastic problem is solved by using an efficient scenario-based decomposition technique, the so-called Progressive Hedging algorithm. This technique is an Augmented-Lagrangian-based decomposition method that decomposes the original problem into smaller subproblems per scenario. The Progressive Hedging algorithm has multiple advantages: — It is easy parallelizable due to its inherent structure. — It provides solution stability and better computational performance compared to Benders-like decomposition techniques (node-based decomposition). — It scales better for large-scale stochastic programming problems. — It has been widely used in the technical literature, thus demonstrating its efficiency. Its implementation has been carried out through the PySP software package which is part of the Coopr open-source Python repository for optimisation. This report also describes the cooling-related constraints included in the unit commitment and dispatch module of Dispa-SET. The cooling-related constraints encompass limitations on allowable maximum water withdrawals of thermal power plants and modelling of the power produced in terms of the river water temperature of the power plant inlet. Limitations on thermal releases or water withdrawals could be imposed due to physical or policy reasons. Finally, an offline and decoupled modelling framework is presented to link such modules with the rainfall-runoff hydrological LISFLOOD model. This modelling framework is able to accurately capture the water-power interactions. Some challenges and barriers to properly address the water-power nexus are also highlighted in the report.JRC.C.7-Knowledge for the Energy Unio

A stochastic dual dynamic programming approach for optimal operation of DER aggregators

The operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees