A looming revolution: Implications of self-generation for the risk exposure of retailers. ESRI WP597, September 2018

Managing the risk associated with uncertain load has always been a challenge for retailers in electricity markets. Yet the load variability has been largely predictable in the past, especially when aggregating a large number of consumers. In contrast, the increasing penetration of unpredictable, small-scale electricity generation by consumers, i.e. self-generation, constitutes a new and yet greater volume risk. Using value-at-risk metrics and Monte Carlo simulations based on German historical loads and prices, the contribution of decentralized solar PV self-generation to retailers’ load and revenue risks is assessed. This analysis has implications for the consumers’ welfare and the overall efficiency of electricity markets

The Future of Power Storage in South Eastern Europe

The European Commission’s Joint Research Centre (JRC) and the Ministry of Energy and Industry of Albania held a joint workshop on the future role of energy storage in South Eastern Europe on 21 -22 October in Tirana. The workshop was attended by 40 specialists from academia, government, regulatory bodies, power industry and consultancies from both EU accession and candidate countries as well as from EU Member States. The participants actively discussed the technical, financial and regulatory challenges of the energy systems of the Western Balkans, and options of how these could be overcome. The event served as a forum for sharing and critically reflecting experience gained in Western Europe during the last decade. The workshop held in Tirana was part of the Enlargement and Integration Action. The present report summarizes the interventions of the participants, the discussions and conclusions of the workshop.JRC.F.6-Energy Technology Policy Outloo

Investigation on electricity market designs enabling demand response and wind generation

Demand Response (DR) comprises some reactions taken by the end-use customers to decrease or shift the electricity consumption in response to a change in the price of electricity or a specified incentive payment over time. Wind energy is one of the renewable energies which has been increasingly used throughout the world. The intermittency and volatility of renewable energies, wind energy in particular, pose several challenges to Independent System Operators (ISOs), paving the way to an increasing interest on Demand Response Programs (DRPs) to cope with those challenges. Hence, this thesis addresses various electricity market designs enabling DR and Renewable Energy Systems (RESs) simultaneously. Various types of DRPs are developed in this thesis in a market environment, including Incentive-Based DR Programs (IBDRPs), Time-Based Rate DR Programs (TBRDRPs) and combinational DR programs on wind power integration. The uncertainties of wind power generation are considered through a two-stage Stochastic Programming (SP) model. DRPs are prioritized according to the ISO’s economic, technical, and environmental needs by means of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The impacts of DRPs on price elasticity and customer benefit function are addressed, including the sensitivities of both DR parameters and wind power scenarios. Finally, a two-stage stochastic model is applied to solve the problem in a mixed-integer linear programming (MILP) approach. The proposed model is applied to a modified IEEE test system to demonstrate the effect of DR in the reduction of operation cost.A Resposta Dinâmica dos Consumidores (DR) compreende algumas reações tomadas por estes para reduzir ou adiar o consumo de eletricidade, em resposta a uma mudança no preço da eletricidade, ou a um pagamento/incentivo específico. A energia eólica é uma das energias renováveis que tem sido cada vez mais utilizada em todo o mundo. A intermitência e a volatilidade das energias renováveis, em particular da energia eólica, acarretam vários desafios para os Operadores de Sistema (ISOs), abrindo caminho para um interesse crescente nos Programas de Resposta Dinâmica dos Consumidores (DRPs) para lidar com esses desafios. Assim, esta tese aborda os mercados de eletricidade com DR e sistemas de energia renovável (RES) simultaneamente. Vários tipos de DRPs são desenvolvidos nesta tese em ambiente de mercado, incluindo Programas de DR baseados em incentivos (IBDRPs), taxas baseadas no tempo (TBRDRPs) e programas combinados (TBRDRPs) na integração de energia eólica. As incertezas associadas à geração eólica são consideradas através de um modelo de programação estocástica (SP) de dois estágios. Os DRPs são priorizados de acordo com as necessidades económicas, técnicas e ambientais do ISO por meio da técnica para ordem de preferência por similaridade com a solução ideal (TOPSIS). Os impactes dos DRPs na elasticidade do preço e na função de benefício ao cliente são abordados, incluindo as sensibilidades dos parâmetros de DR e dos cenários de potência eólica. Finalmente, um modelo estocástico de dois estágios é aplicado para resolver o problema numa abordagem de programação linear inteira mista (MILP). O modelo proposto é testado num sistema IEEE modificado para demonstrar o efeito da DR na redução do custo de operação

Optimal management of a virtual power plant with photovoltaic and power-to-gas to exploit the benefit of value stacking from crossmarket arbitrage

The energy sector is facing a transformation, the traditional business model for electricity generated by large, centralised plants with limited customer engagement and standardized supply contracts is fading away. The restyling of the electricity markets is a consequence of several factors: the liberalisation of the electricity sector begun around 20 years ago in Italy; the growth of the intermittent and unpredictable renewable technologies thanks to lower costs and larger investments than fossil fuels ones; the spread of distributed generation that makes the consumer able to produce energy too, which makes him an active player in the market by becoming a so-called prosumer. In this context, given the dynamism to which the electricity market is subjected, it is interesting to study the economic feasibility of enhanced bidding strategies from the point of view of the manager of a plant consisting of photovoltaic and Power-to-Gas. The starting point of this thesis is a code formulated by the research group from the Department of Industrial Engineering at the University of Padua which comprehends Jan Marc Schwidtal, Marco Agostini, Massimiliano Coppo, Fabio Bignucolo and Arturo Lorenzoni. Specifically, the research work models the operation of a virtually aggregated plant by highlighting the opportunities arising from the value stacking in terms of progressive market penetration of this unit. It evaluates energy flows and financial results on annual basis, taking into account technical constraints of the photovoltaic generation and of the Power-to-gas specifications. In this thesis, changes have been introduced concerning only the description of the economic side of the model and not the technical one. The idea is to implement an enhanced optimization approach to formulate a combined bidding strategy across the energy markets and the auxiliary services markets, exploiting the concept of cross-market arbitrage: this method regards in particular the intraday and balancing markets and consists in buying and subsequently reselling the same type of energy in the same quantity at two different prices. Four different operating modes with a gradual and increasing integration in the markets are studied and the respective optimization problems are solved using the Gurobi solver through the Yalmip toolbox installed within the Matlab software. Lastly, considerations were drawn about the risk management that could affect the manager of the unit by investigating how far it is possible to go in adopting this bidding strategy while operating the plant.The energy sector is facing a transformation, the traditional business model for electricity generated by large, centralised plants with limited customer engagement and standardized supply contracts is fading away. The restyling of the electricity markets is a consequence of several factors: the liberalisation of the electricity sector begun around 20 years ago in Italy; the growth of the intermittent and unpredictable renewable technologies thanks to lower costs and larger investments than fossil fuels ones; the spread of distributed generation that makes the consumer able to produce energy too, which makes him an active player in the market by becoming a so-called prosumer. In this context, given the dynamism to which the electricity market is subjected, it is interesting to study the economic feasibility of enhanced bidding strategies from the point of view of the manager of a plant consisting of photovoltaic and Power-to-Gas. The starting point of this thesis is a code formulated by the research group from the Department of Industrial Engineering at the University of Padua which comprehends Jan Marc Schwidtal, Marco Agostini, Massimiliano Coppo, Fabio Bignucolo and Arturo Lorenzoni. Specifically, the research work models the operation of a virtually aggregated plant by highlighting the opportunities arising from the value stacking in terms of progressive market penetration of this unit. It evaluates energy flows and financial results on annual basis, taking into account technical constraints of the photovoltaic generation and of the Power-to-gas specifications. In this thesis, changes have been introduced concerning only the description of the economic side of the model and not the technical one. The idea is to implement an enhanced optimization approach to formulate a combined bidding strategy across the energy markets and the auxiliary services markets, exploiting the concept of cross-market arbitrage: this method regards in particular the intraday and balancing markets and consists in buying and subsequently reselling the same type of energy in the same quantity at two different prices. Four different operating modes with a gradual and increasing integration in the markets are studied and the respective optimization problems are solved using the Gurobi solver through the Yalmip toolbox installed within the Matlab software. Lastly, considerations were drawn about the risk management that could affect the manager of the unit by investigating how far it is possible to go in adopting this bidding strategy while operating the plant

Techno-economic evaluation of battery storage systems in industry

In the context of a changing energy system towards one dominated by renewable energy sources, the demand for flexible energy generation and consumption will increase. Battery storage systems can provide a significant share of this energy flexibility, especially when combined with an industrial manufacturing plant to shift the industrial electricity demand over time. This paper contributes to a better understanding of the business decision when investing in a battery storage system and when marketing energy flexibility. For this purpose, the work considers the techno-economic and regulatory framework for flexibility measures and examines the optimal investment and dispatch planning for a battery storage system in an industrial company. The studies in this thesis focus on three central aspects. As a first aspect, the various revenue streams for the stored electricity are analysed and how these influence the profitability of a battery storage system. In particular, the provision of frequency containment reserve power, peak load shifting or peak shaving, arbitrage trading on the energy markets and the increase in self-consumption through photovoltaic self-generation are addressed. For this purpose, an optimisation model is formulated as a discrete, linear programme that maps the economic framework of the flexibility markets and integrates the technological constraints of the battery storage system. As a second aspect, uncertainties about market prices, load and generation behaviour are integrated into the optimisation model and the influence on the investment decision is investigated. This is done on the one hand by a two-stage robust optimisation model, which represents the uncertainty about the market success on the intraday market. On the other hand, the significance of the sequence of uncertain market decisions is illuminated through a multi-stage stochastic optimisation model. As a third aspect, the trade-off between the economic and ecological use of a battery storage system is analysed. For this purpose, an ecological, CO₂-minimal dispatch is calculated by deriving national CO₂-emission factors and compared with an economically optimal dispatch. The case studies are analysed based on real industrial load data from small, medium and large enterprises. The thesis discusses the technical and economic framework conditions, with the main focus on Germany. However, a comparison between the countries Germany, Denmark, and Croatia is also presented. The results show that peak shaving and the provision of frequency containment reserve are complementary and make the investment in a battery storage system economically viable. Self-generation through a photovoltaic system can reduce the risk arising from uncertain energy market prices. However, the sequence of uncertain decisions has a significant impact on the design of the battery storage system. Economically feasible operation through arbitrage trading, on the other hand, is not possible due to the small price differences in the markets and limitations due to battery ageing and efficiency. These battery characteristics also influence the use of a battery storage system for CO₂-reduction. Due to the limited number of cycles and relatively high charging losses, battery technology is currently unsuitable for CO₂-minimal storage use. Nevertheless, the economic and ecological potential of battery storage systems strongly depends on individual factors such as local grid charges, the selected battery technology and the individual industrial load profile. Advances in battery technology, such as increased lifetime, and possible new flexibility markets, such as dynamic grid charges, offer new application and marketing opportunities that could increase the economic viability of a battery storage system

Power and Energy Student Summit 2019: 9 – 11 July 2019 Otto von Guericke University Magdeburg ; Conference Program

The book includes a short description of the conference program of the "Power and Energy Student Summit 2019". The conference, which is orgaized for students in the area of electric power systems, covers topics such as renewable energy, high voltage technology, grid control and network planning, power quality, HVDC and FACTS as well as protection technology. Besides the overview of the conference venue, activites and the time schedule, the book includes all papers presented at the conference

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

Mathematical programming-based models for the distribution networks' decarbonization

(English) Climate change is pushing to decarbonize worldwide economies and forcing fossil fuel-based power systems to evolve into power systems based mainly on renewable energies sources (RES). Thus, increasing the energy generated from renewables in the energy supply mix involves transversal challenges at operational, market, political and social levels due to the stochasticity associated with these technologies and their capacity to generate energy at a small scale close to the consumption point. In this regard, the power generation uncertainty can be handled through battery storage systems (BSS) that have become competitive over the last few years due to a significant price reduction and are a potential alternative to mitigate the technical network problems associated with the intermittency of the renewables, providing flexibility to store/supply energy when is required. On the other hand, the capacity of low-cost generation from small-scale power systems (distributed or decentralized generation (DG)) represents an opportunity for both customers and the power system operators. i.e., customers can generate their energy, reduce their network dependency, and participate actively in eventual local energy markets (LEM), while the power system operator can reduce the system losses and increase the power system quality against unexpected external failures. Nevertheless, incorporating these structures and operational frameworks into distribution networks (DN) requires developing sophisticated tools to support decision-making related to the optimal integration of the distributed energy resources (DER) and assessing the performance of new DNs with high DERs penetration under different operational scenarios. This thesis addresses the distribution networks' decarbonization challenge by developing novel algorithms and applying different optimization techniques through three subtopics. The first axis addresses the optimal sizing and allocation of DG and BSS into a DN from deterministic and stochastic approaches, considering the technical network limitation, the electric vehicle (EV) presence, the users capacity to modify their load consumption, and the DG capability to generate reactive power for voltage stability. Besides, a novel algorithm is developed to solve the deterministic and stochastic models for multiple scenarios providing an accurate DERs capacity that should be installed to decrease the external network dependency. The second subtopic assesses the DN capacity to face unlikely scenarios like primary grid failure or natural disasters preventing the energy supply through a deterministic model that modifies the unbalance DN topology into multiple virtual microgrids (VM) balanced, considering the power supplied by DG and the flexibility provided by the storage devices (SD) and demand response (DR). The third axis addresses the emerging transactive energy (TE) schemes in DNs with high DERs penetration at a residential level through two stochastic approaches to model a Peer-to-peer (P2P) energy trading. To this end, the capability of a P2P energy trading scheme to operate on different markets as day-ahead, intraday, flexibility, and ancillary services (AS) market is assessed, while an algorithm is developed to manage the users' information under a decentralized design.(Català) El cambio climático está obligando a descarbonizar las economías de todo el mundo forzando a los sistemas de energía basados en combustibles fósiles a evolucionar hacia sistemas de energía basados principalmente en fuentes de energía renovables (FER). Así, incrementar la energía generada a partir de renovables en el mix energético está implicando retos transversales a nivel operativo, de mercado, político y social debido a la estocasticidad asociada a estas tecnologías y su capacidad de generar electricidad a pequeña escala cerca al punto de consumo. En este sentido, la incertidumbre en la generación de energía eléctrica puede ser manejada a través de sistemas de almacenamiento en baterías (BSS) que se han vuelto competitivos en los últimos años debido a una importante reducción de precios y son una potencial alternativa para mitigar los problemas técnicos de red asociados a la intermitencia de las renovables, proporcionando flexibilidad para almacenar/suministrar energía cuando sea necesario. Por otro lado, la capacidad de generación a bajo costo a partir de sistemas eléctricos de pequeña escala (generación distribuida o descentralizada (GD)) representa una oportunidad tanto para los clientes como para los operadores del sistema eléctrico. Es decir, los clientes pueden generar su energía, reducir su dependencia de la red y participar activamente en eventuales mercados locales de energía (MLE), mientras que el operador del sistema eléctrico puede reducir las pérdidas del sistema y aumentar la calidad del sistema eléctrico frente a fallas externas inesperadas. Sin embargo, incorporar estas estructuras y marcos operativos en las redes de distribución (RD) requiere desarrollar herramientas sofisticadas para apoyar la toma de decisiones relacionadas con la integración óptima de los recursos energéticos distribuidos (RED) y evaluar el desempeño de las nuevas RD con alta penetración de RED bajo diferentes escenarios de operación. Esta tesis aborda el desafío de la descarbonización de las redes de distribución mediante el desarrollo de algoritmos novedosos y la aplicación de diferentes técnicas de optimización a través de tres dimensiones. El primer eje aborda el dimensionamiento y localización óptimos de GD y BSS en una RD desde enfoques determinísticos y estocásticos, considerando la limitación técnica de la red, la presencia de vehículos eléctricos (VE), la capacidad de los usuarios para modificar su consumo de carga y la capacidad de GD para generar potencia reactiva para la estabilidad del voltaje. Además, se desarrolla un algoritmo novedoso para resolver los modelos determinísticos y estocásticos para múltiples escenarios proporcionando una capacidad precisa de RED que debe instalarse para disminuir la dependencia de la red externa. El segundo subtema evalúa la capacidad de la RD para enfrentar escenarios improbables como fallas en la red primaria o desastres naturales que impidan el suministro de energía, a través de un modelo determinista que modifica la topología de la RD desequilibrada en múltiples microrredes virtuales (MV) balanceadas, considerando la potencia suministrada por GD y la flexibilidad proporcionada por los dispositivos de almacenamiento y respuesta a la demanda (DR). El tercer eje aborda los esquemas emergentes de energía transactiva en RDs con alta penetración de RED a nivel residencial a través de dos enfoques estocásticos para modelar un comercio de energía Peer-to-peer (P2P). Para ello, se evalúa la capacidad de un esquema de comercialización de energía P2P para operar en diferentes mercados como el mercado diario, intradiario, de flexibilidad y de servicios complementarios, a la vez que se desarrolla un algoritmo para gestionar la información de los usuarios bajo un esquema descentralizado.Postprint (published version

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.publishedVersio