Optimizing energy market participation with batteries

Due to the fact that the energy sector is in transition, there are goals for lowering the energy cost with the use of renewables and batteries. This presents challenges to the system and the solution is the issuing of energy communities that can be used to make electricity provision more clean and secure. It is also to see how energy flexibility elements or elements on the consumption side can make the system more efficient and cheaper, which is being done in this paper concerning the day-ahead bid and batteries. Traditional day-ahead bidding methods have become costly, mainly when the forecasted energy consumption differs from the actual consumption, which has to be resolved by penalizing with an imbalance cost. This thesis is part of a more significant project (Layered Energy System) that is to be deployed in Spain. Applying such changes to the electricity system first requires becoming familiar with and understanding Spain's context. The first part of this thesis provides research to understand the Spanish regulatory framework, how the market works, and the status of these technologies in Spain. Following that, this thesis's primary work is to explore how day-ahead market bid could be improved through the use of batteries for better planning and error assumptions. It mentions several day-ahead bidding strategies in the context of energy and batteries. And then selects a subset (three) of the studied strategies and implements them, comparing their performance on actual electricity data. Finally, selects the one that best fits various scenarios and requirements. A particular objective function is opted to be minimized with respect to the battery constraints that involve the variables. A linear program will find the values that best fits those variables at every time step $t$ of a single day. The methodology is an improvement over traditional predictive models. After comparing different strategies, Results show that strategy one, namely "Stochastic Chance-constraint optimization", yields the best results. In this strategy, the battery would have the freedom to maximize profit even if it sometimes increases imbalance. The preferred error distribution for this strategy is the Gamma distribution. Using a battery to offset imbalances can help to minimize total energy cost for a whole day (up to 26%). The last part of the thesis is ongoing research about capacity traders and market performance. It surveys the literature on trading strategies in various contexts and markets relevant to capacity traders. The market performance in capacity trading needs to consider how well the buildings can reach their desired capacity through bidding and selling. Performance metrics that are typically used to evaluate those trading strategies were documented. This feature is being worked on with python, but it will not be able to be shown

Strategic development in the petrochemical industry

Imperial Users onl

Grid Scale Battery Energy Storage Investment Potential - Analysis and Simulations of Frequency Control Markets in Germany and the UK

The need for energy storages in future power systems is acknowledged both in literature and in industry. Simultaneously battery energy storage technologies, especially Lithium-ion, are seen technologically relatively mature with favorable cost development. Whereas frequency control markets provide exploitable commercial and technical framework for battery investment. Nevertheless, true commercial viability is still uncertain in leading European markets in Germany and the UK. The purpose of the study was to provide complete and comparative market analysis and demonstrated prospective investment profitability outcomes for grid scale battery energy storages in Germany and the UK. In addition, the study aimed to show required conditions for desired investment performances. The study explored investment potential in primary frequency control market in Germany and enhanced frequency response market in the UK by analyzing market attractiveness from multiple aspects. The countries were ranked based on the analyzed aspects by Analytical Hierarchy Process. Finally, financial Monte Carlo investment simulations with revenue and cost uncertainties were performed. Simulations also provided required conditions for profitability. Analyzed data was based on historical market data, performed online market research and literature. Key findings of the study revealed that the chosen markets form suitable commercial framework for battery investments, but Germany shows clearly higher potential. However, the potential was questionable since both markets face significant challenges especially in financial sense. The concerns were confirmed by the simulations which suggested around 1–5 % and -3–3 % internal rate of return levels for Germany and the UK respectively. In addition, reaching 6 % return was seen very challenging whilst over 10 % return levels seemed unrealistic in the UK and extremely optimistic for Germany. The overall conclusion was that battery energy storage investment in either of the markets cannot currently be justified primarily by financial returns but needs strategic support.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Supercomputing futures : the next sharing paradigm for HPC resources : economic model, market analysis and consequences for the Grid

À la croisée des chemins du génie informatique, de la finance et de l'économétrie, cette thèse se veut fondamentalement un exercice en ingénierie économique dont l' objectif est de contribuer un système novateur, durable et adaptatif pour le partage de resources de calcul haute-performance. Empruntant à la finance fondamentale et à l'analyse technique, le modèle proposé construit des ratios et des indices de marché à partir de statistiques transactionnelles. Cette approche, encourageant les comportements stratégiques, pave la voie à une métaphore de partage plus efficace pour la Grid, où l'échange de ressources se voit maintenant pondéré. Le concept de monnaie de Grid, un instrument beaucoup plus liquide et utilisable que le troc de resources comme telles est proposé: les Grid Credits. Bien que les indices proposés ne doivent pas être considérés comme des indicateurs absolus et contraignants, ils permettent néanmoins aux négociants de se faire une idée de la valeur au marché des différentes resources avant de se positionner. Semblable sur de multiples facettes aux bourses de commodités, le Grid Exchange, tel que présenté, permet l'échange de resources via un mécanisme de double-encan. Néanmoins, comme les resources de super-calculateurs n'ont rien de standardisé, la plate-forme permet l'échange d'ensemble de commodités, appelés requirement sets, pour les clients, et component sets, pour les fournisseurs. Formellement, ce modèle économique n'est qu'une autre instance de la théorie des jeux non-coopératifs, qui atteint éventuellement ses points d'équilibre. Suivant les règles du "libre-marché", les utilisateurs sont encouragés à spéculer, achetant, ou vendant, à leur bon vouloir, l'utilisation des différentes composantes de superordinateurs. En fin de compte, ce nouveau paradigme de partage de resources pour la Grid dresse la table à une nouvelle économie et une foule de possibilités. Investissement et positionnement stratégique, courtiers, spéculateurs et même la couverture de risque technologique sont autant d'avenues qui s'ouvrent à l'horizon de la recherche dans le domaine

Operational research and simulation methods for autonomous ride-sourcing

Ride-sourcing platforms provide on-demand shared transport services by solving decision problems related to ride-matching and pricing. The anticipated commercialisation of autonomous vehicles could transform these platforms to fleet operators and broaden their decision-making by introducing problems such as fleet sizing and empty vehicle redistribution. These problems have been frequently represented in research using aggregated mathematical programs, and alternative practises such as agent-based models. In this context, this study is set at the intersection between operational research and simulation methods to solve the multitude of autonomous ride-sourcing problems. The study begins by providing a framework for building bespoke agent-based models for ride-sourcing fleets, derived from the principles of agent-based modelling theory, which is used to tackle the non-linear problem of minimum fleet size. The minimum fleet size problem is tackled by investigating the relationship of system parameters based on queuing theory principles and by deriving and validating a novel model for pickup wait times. Simulating the fleet function in different urban areas shows that ride-sourcing fleets operate queues with zero assignment times above the critical fleet size. The results also highlight that pickup wait times have a pivotal role in estimating the minimum fleet size in ride-sourcing operations, with agent-based modelling being a more reliable estimation method. The focus is then shifted to empty vehicle redistribution, where the omission of market structure and underlying customer acumen, compromises the effectiveness of existing models. As a solution, the vehicle redistribution problem is formulated as a non-linear convex minimum cost flow problem that accounts for the relationship of supply and demand of rides by assuming a customer discrete choice model and a market structure. An edge splitting algorithm is then introduced to solve a transformed convex minimum cost flow problem for vehicle redistribution. Results of simulated tests show that the redistribution algorithm can significantly decrease wait times and increase profits with a moderate increase in vehicle mileage. The study is concluded by considering the operational time-horizon decision problems of ride-matching and pricing at periods of peak travel demand. Combinatorial double auctions have been identified as a suitable alternative to surge pricing in research, as they maximise social welfare by relying on stated customer and driver valuations. However, a shortcoming of current models is the exclusion of trip detour effects in pricing estimates. The study formulates a shared-ride assignment and pricing algorithm using combinatorial double auctions to resolve the above problem. The model is reduced to the maximum weighted independent set problem, which is APX-hard. Therefore, a fast local search heuristic is proposed, producing solutions within 10\% of the exact approach for practical implementations.Open Acces

Utilization of Electric Prosumer Flexibility Incentivized by Spot and Balancing Markets

The use of energy flexibility to balance electricity demand and supply is becoming increasingly important due to the growing share of fluctuating energy sources. Electric flexibility regarding time or magnitude of consumption can be offered in the form of different products on electricity spot and balancing power markets. In the wake of the energy transition and because of new possibilities provided by digitalization, the decision intervals on these markets are becoming shorter and the controllability of electricity consumption and generation more small-scale. This evolution opens up new chances for formerly passive energy consumers. This thesis shows how electric flexibility can be monetized using the application example of commercial sites. These are often multimodal energy systems coupling electricity, heat, and gas, and thus deliver high flexibility potential. To leverage this potential, a comprehensive picture of demand-side flexibilization is provided and used to propose an energy management system and optimization for cost-optimized device schedules. The cost-optimization considers two simultaneous incentives: variable day-ahead spot market prices and revenues for offering possible schedule adjustments to the automatic Frequency Restoration Reserve (aFRR) balancing market. To solve the formulated optimization problem, a genetic algorithm is presented, tailored to the specific needs of consumers. In addition to addressing the trade-off between the two competing markets, the algorithm inherently considers the uncertain activation of aFRR bids and related catch-up effects. An analysis of the activation behavior of aFRR balancing market bids, based on a developed ex-post simulation, forms an important decision basis for the optimization. Finally, a simulation study concentrating on battery energy storage systems and combined heat and power plants on the consumer side enables the quantitative discussion of the optimization potential. The results show that consumers considering both markets simultaneously can achieve cost benefits that are up to multiples of those for pure day-ahead price optimization, despite the stochastic nature of aFRR balancing power activations. In conclusion, this thesis enables formerly passive electricity consumers to assume the role of alternative balancing service providers, hence contributing to the economic and reliable operation of power grids characterized by a high share of renewable energy sources

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order

Contingency Management in Power Systems and Demand Response Market for Ancillary Services in Smart Grids with High Renewable Energy Penetration.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017