Forecasting different dimensions of liquidity in the intraday electricity markets: A review

Energy consumption increases daily across the world. Electricity is the best means that humankind has found for transmitting energy. This can be said regardless of its origin. Energy transmission is crucial for ensuring the efficient and reliable distribution of electricity from power generation sources to end-users. It forms the backbone of modern societies, supporting various sectors such as residential, commercial, and industrial activities. Energy transmission is a fundamental enabler of well-functioning and competitive electricity markets, supporting reliable supply, market integration, price stability, and the integration of renewable energy sources. Electric energy sourced from various regions worldwide is routinely traded within these electricity markets on a daily basis. This paper presents a review of forecasting techniques for intraday electricity markets prices, volumes, and price volatility. Electricity markets operate in a sequential manner, encompassing distinct components such as the day-ahead, intraday, and balancing markets. The intraday market is closely linked to the timely delivery of electricity, as it facilitates the trading and adjustment of electricity supply and demand on the same day of delivery to ensure a balanced and reliable power grid. Accurate forecasts are essential for traders to maximize profits within intraday markets, making forecasting a critical concern in electricity market management. In this review, statistical and econometric approaches, involving various machine learning and ensemble/hybrid techniques, are presented. Overall, the literature highlights the superiority of machine learning and ensemble/hybrid models over statistical models

Demand response through automated air conditioning in commercial buildings - a data-driven approach

Building operation faces great challenges in electricity cost control as prices on electricity markets become increasingly volatile. Simultaneously, building operators could nowadays be empowered with information and communication technology that dynamically integrates relevant information sources, predicts future electricity prices and demand, and uses smart control to enable electricity cost savings. In particular, data-driven decision support systems would allow the utilization of temporal flexibilities in electricity consumption by shifting load to times of lower electricity prices. To contribute to this development, we propose a simple, general, and forward-looking demand response (DR) approach that can be part of future data-driven decision support systems in the domain of building electricity management. For the special use case of building air conditioning systems, our DR approach decides in periodic increments whether to exercise air conditioning in regard to future electricity prices and demand. The decision is made based on an ex-ante estimation by comparing the total expected electricity costs for all possible activation periods. For the prediction of future electricity prices, we draw on existing work and refine a prediction method for our purpose. To determine future electricity demand, we analyze historical data and derive data-driven dependencies. We embed the DR approach into a four-step framework and demonstrate its validity, utility and quality within an evaluation using real-world data from two public buildings in the US. Thereby, we address a real-world business case and find significant cost savings potential when using our DR approach

Two-Stage Stochastic Program Optimizing the Total Cost of Ownership of Electric Vehicles in Commercial Fleets

The possibility of electric vehicles to technically replace internal combustion engine vehicles and to deliver economic benefits mainly depends on the battery and the charging infrastructure as well as on annual mileage (utilizing the lower variable costs of electric vehicles). Current studies on electric vehicles’ total cost of ownership often neglect two important factors that influence the investment decision and operational costs: firstly, the trade-off between battery and charging capacity; secondly the uncertainty in energy consumption. This paper proposes a two-stage stochastic program that minimizes the total cost of ownership of a commercial electric vehicle under uncertain energy consumption and available charging times induced by mobility patterns and outside temperature. The optimization program is solved by sample average approximation based on mobility and temperature scenarios. A hidden Markov model is introduced to predict mobility demand scenarios. Three scenario reduction heuristics are applied to reduce computational effort while keeping a high-quality approximation. The proposed framework is tested in a case study of the home nursing service. The results show the large influence of the uncertain mobility patterns on the optimal solution. In the case study, the total cost of ownership can be reduced by up to 3.9% by including the trade-off between battery and charging capacity. The introduction of variable energy prices can lower energy costs by 31.6% but does not influence the investment decision in this case study. Overall, this study provides valuable insights for real applications to determine the techno-economic optimal electric vehicle and charging infrastructure configuration

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

Methods for optimization of a German TSO’s electricity market performance with special attention to wind power

Diese Dissertation beschäftigt sich mit dem Thema der Optimierung des Marktverhaltens eines Übertragungsnetzbetreibers (ÜNB). Dabei ist die Position eines Marktteilnehmers relativ neu für den ÜNB. Sie entstand erst in den letzten Jahren infolge des Strebens der deutschen Regierung, die Abhängigkeit des nationalen Energiesystems von den Energieimporten zu reduzieren und dieses umweltfreundlicher zu gestalten. Demzufolge wurde der ÜNB dazu verpflichtet, all die Einspeisungen von den Quellen der erneuerbaren Energie aufzunehmen und diese zu vermarkten. Dabei wurde der ÜNB mit den speziellen Charakteristika des neuen „Marktprodukts“ konfrontiert: durch den hohen Anteil der stochastischen Windenergie an dem aufgenommenen Energiemix, wird die Verlässlichkeit eines solchen Marktgeschäfts gefährdet. Damit die aufgenommenen Windeinspeisungen optimal vermarktet werden können, muss der ÜNB über eine zuverlässige day-ahead Prognose verfügen. Deutsche ÜNBs verwenden dabei eine Metaprognose, gewichtet von den mehreren Windenergieprognosen, die in den letzten Jahren entwickelt wurden. Die Mehrheit dieser Prognosen basiert auf numerischen Wettervorhersagemodellen, welche die ÜNBs über die in jedem Zeitpunkt zu erwartende Windenergiemenge informieren. Damit reduziert sich weitgehend die der Windenergie zugeordnete Zufälligkeit. Nichtsdestotrotz sind die verbleibenden Abweichungen zwischen der day-ahead Prognose und den tatsächlich auftretenden Windeinspeisungen der Grund für den immensen zusätzlichen Kostenaufwand wie für den ÜNB (z.B. für die Leistungsvorhaltung und das Einsetzen der Regelenergie) als auch für den Letztverbraucher (erhöhte Elektrizitätstarife infolge der Umwälzung der genannten Zusatzkosten). Eine sinnvolle Maßnahme um diesen Abweichungen entgegenzuwirken wäre, die Qualität der day-ahead Prognose zu verbessern. Dementsprechend groß ist die Forschungsgemeinschaft, die sich mit dieser Fragenstellung auseinandersetzt. Der ÜNB an sich hat allerdings keine Möglichkeit die potentiellen Fehlerquellen zu beeinflussen. Die begrenzten Informationen, die er zur Verfügung hat (die gewichtete day-ahead Prognose und Online-Hochrechnungsdaten, die er bekommt mit der Verzögerung von 24 Stunden) zwingen den ÜNB, solche Methoden für die Optimierung seines Marktverhaltens aufzusuchen, die mit diesen wenigen Angaben arbeiten können. Genau diese Tatsache macht die vorliegende Arbeit neuartig, da die präsentierten Methoden – Q-Learning und Kalman-Filter – diesen Anforderungen entsprechen. Ihre Leistung wird binnen des nachsimulierten EEG-Ausgleichsmechanismus getestet und anhand von realen Windeinspeisungsdaten verifiziert. Die erreichten Ergebnisse werden mit den üblichen statistischen Kennwerten bewertet.This thesis is devoted to the search of the methods to optimize the market performance of a Transmission System Operator (TSO). The position of a TSO as a market player is a quite new one, since its traditional obligations consist in ensuring of network availability, congestion prevention and management, ensuring the system stability. It is emerged from the pursuit of German government of reducing the dependency of national energy system on energy imports and environmental and climate protection. In these circumstances a TSO was obliged to assume all the energy produced by renewable energy sources (RES) to bid it further on the energy market. Thereby it is faced with the special characteristics of this "market product": due to the significant share of stochastic wind power in the assumed energy mix the reliability of its trading operations becomes risky. In order to bring the wind power feed-in a TSO receives in line with the regulations of German energy market, it must have a trustworthy day-ahead forecast. German TSOs use by their operation the weighted average of several wind power forecast tools developed in the recent years. The majority of them is based on numerical weather predictions and provides the information how much wind power can be expected at each point of time. Thus they announce the variations in the electricity production of wind farms in advance and largely reduce the degree of randomness attributed to wind energy. However there are still deviations to be observed between the day-ahead forecast and real wind power feed-in. These deviations result in significant costs both for TSOs (i.e. for provision and application of control energy) and end-customers (increased electricity tariffs due to additional balancing costs of TSOs). The reasonable measure to countervail these problems is the improvement of the day-ahead forecast a TSO receives as a service. Respectively the research community occupied with the search of the adequate solutions is rather meaningful. However, a TSO as a recipient of a day-ahead forecast does not have any possibility to influence the potential sources of forecast inaccuracy. It needs therefore a solution that could optimize its day-ahead market operation regarding the limited information resources it has: the weighted day-ahead wind power forecast it receives as a service and the real-time values of wind power feed-in that it is given in 24-hours-delay. This consideration turns the current research topic into the rather novel one. Two alternative methods to solve the mentioned problem are proposed: Q-Learning and Kalman filter. Their performance is tested within the simulated model of German RES-equalisation scheme and verified with the real-life data of wind energy feed-in. Achieved results are evaluated with the common accepted error measures

Methods for optimization of a German TSO’s electricity market performance with special attention to wind power

Diese Dissertation beschäftigt sich mit dem Thema der Optimierung des Marktverhaltens eines Übertragungsnetzbetreibers (ÜNB). Dabei ist die Position eines Marktteilnehmers relativ neu für den ÜNB. Sie entstand erst in den letzten Jahren infolge des Strebens der deutschen Regierung, die Abhängigkeit des nationalen Energiesystems von den Energieimporten zu reduzieren und dieses umweltfreundlicher zu gestalten. Demzufolge wurde der ÜNB dazu verpflichtet, all die Einspeisungen von den Quellen der erneuerbaren Energie aufzunehmen und diese zu vermarkten. Dabei wurde der ÜNB mit den speziellen Charakteristika des neuen „Marktprodukts“ konfrontiert: durch den hohen Anteil der stochastischen Windenergie an dem aufgenommenen Energiemix, wird die Verlässlichkeit eines solchen Marktgeschäfts gefährdet. Damit die aufgenommenen Windeinspeisungen optimal vermarktet werden können, muss der ÜNB über eine zuverlässige day-ahead Prognose verfügen. Deutsche ÜNBs verwenden dabei eine Metaprognose, gewichtet von den mehreren Windenergieprognosen, die in den letzten Jahren entwickelt wurden. Die Mehrheit dieser Prognosen basiert auf numerischen Wettervorhersagemodellen, welche die ÜNBs über die in jedem Zeitpunkt zu erwartende Windenergiemenge informieren. Damit reduziert sich weitgehend die der Windenergie zugeordnete Zufälligkeit. Nichtsdestotrotz sind die verbleibenden Abweichungen zwischen der day-ahead Prognose und den tatsächlich auftretenden Windeinspeisungen der Grund für den immensen zusätzlichen Kostenaufwand wie für den ÜNB (z.B. für die Leistungsvorhaltung und das Einsetzen der Regelenergie) als auch für den Letztverbraucher (erhöhte Elektrizitätstarife infolge der Umwälzung der genannten Zusatzkosten). Eine sinnvolle Maßnahme um diesen Abweichungen entgegenzuwirken wäre, die Qualität der day-ahead Prognose zu verbessern. Dementsprechend groß ist die Forschungsgemeinschaft, die sich mit dieser Fragenstellung auseinandersetzt. Der ÜNB an sich hat allerdings keine Möglichkeit die potentiellen Fehlerquellen zu beeinflussen. Die begrenzten Informationen, die er zur Verfügung hat (die gewichtete day-ahead Prognose und Online-Hochrechnungsdaten, die er bekommt mit der Verzögerung von 24 Stunden) zwingen den ÜNB, solche Methoden für die Optimierung seines Marktverhaltens aufzusuchen, die mit diesen wenigen Angaben arbeiten können. Genau diese Tatsache macht die vorliegende Arbeit neuartig, da die präsentierten Methoden – Q-Learning und Kalman-Filter – diesen Anforderungen entsprechen. Ihre Leistung wird binnen des nachsimulierten EEG-Ausgleichsmechanismus getestet und anhand von realen Windeinspeisungsdaten verifiziert. Die erreichten Ergebnisse werden mit den üblichen statistischen Kennwerten bewertet.This thesis is devoted to the search of the methods to optimize the market performance of a Transmission System Operator (TSO). The position of a TSO as a market player is a quite new one, since its traditional obligations consist in ensuring of network availability, congestion prevention and management, ensuring the system stability. It is emerged from the pursuit of German government of reducing the dependency of national energy system on energy imports and environmental and climate protection. In these circumstances a TSO was obliged to assume all the energy produced by renewable energy sources (RES) to bid it further on the energy market. Thereby it is faced with the special characteristics of this "market product": due to the significant share of stochastic wind power in the assumed energy mix the reliability of its trading operations becomes risky. In order to bring the wind power feed-in a TSO receives in line with the regulations of German energy market, it must have a trustworthy day-ahead forecast. German TSOs use by their operation the weighted average of several wind power forecast tools developed in the recent years. The majority of them is based on numerical weather predictions and provides the information how much wind power can be expected at each point of time. Thus they announce the variations in the electricity production of wind farms in advance and largely reduce the degree of randomness attributed to wind energy. However there are still deviations to be observed between the day-ahead forecast and real wind power feed-in. These deviations result in significant costs both for TSOs (i.e. for provision and application of control energy) and end-customers (increased electricity tariffs due to additional balancing costs of TSOs). The reasonable measure to countervail these problems is the improvement of the day-ahead forecast a TSO receives as a service. Respectively the research community occupied with the search of the adequate solutions is rather meaningful. However, a TSO as a recipient of a day-ahead forecast does not have any possibility to influence the potential sources of forecast inaccuracy. It needs therefore a solution that could optimize its day-ahead market operation regarding the limited information resources it has: the weighted day-ahead wind power forecast it receives as a service and the real-time values of wind power feed-in that it is given in 24-hours-delay. This consideration turns the current research topic into the rather novel one. Two alternative methods to solve the mentioned problem are proposed: Q-Learning and Kalman filter. Their performance is tested within the simulated model of German RES-equalisation scheme and verified with the real-life data of wind energy feed-in. Achieved results are evaluated with the common accepted error measures

Engineering Local Electricity Markets for Residential Communities

In line with the progressing decentralization of electricity generation, local electricity markets (LEMs) support electricity end customers in becoming active market participants instead of passive price takers. They provide a market platform for trading locally generated (renewable) electricity between residential agents (consumers, prosumers, and producers) within their community. Based on a structured literature review, a market engineering framework for LEMs is developed. The work focuses on two of the framework\u27s eight components, namely the agent behavior and the (micro) market structure. Residential agent behavior is evaluated in two steps. Firstly, two empirical studies, a structural equation model-based survey with 195 respondents and an adaptive choice-based conjoint study with 656 respondents, are developed, conducted and evaluated. Secondly, a discount price LEM is designed following the surveys\u27 results. Theoretical solutions of the LEM bi-level optimization problem with complete information and heuristic reinforcement learning with incomplete information are investigated in a multi-agent simulation to find the profit-maximizing market allocations. The (micro) market structure is investigated with regards to LEM business models, information systems and real-world application projects. Potential business models and their characteristics are combined in a taxonomy based on the results of 14 expert interviews. Then, the Smart Grid Architecture Model is utilized to derive the organizational, informational, and technical requirements for centralized and distributed information systems in LEMs. After providing an overview on current LEM implementations projects in Germany, the Landau Microgrid Project is used as an example to test the derived requirements. In conclusion, the work recommends current LEM projects to focus on overall discount electricity trading. Premium priced local electricity should be offered to subgroups of households with individual higher valuations for local generation. Automated self-learning algorithms are needed to mitigate the trading effort for residential LEM agents in order to ensure participation. The utilization of regulatory niches is suggested until specific regulations for LEMs are established. Further, the development of specific business models for LEMs should become a prospective (research) focus

Design, modelling and valuation of innovative dispatch strategies for energy storage systems

Energy storage has in recent years attracted considerable interest, mainly owing to its potential to support large-scale integration of renewable energy sources (RES). At the same time however, energy storage technologies are called to take over multiple roles across the entire electricity sector, introducing modern applications for both private actors and system operators. In this context, the current thesis focuses on the valuation of emerging energy storage applications, while also proceeding to the design and modelling of novel dispatch strategies, along with the development of financial instruments and support measures for the market uptake of energy storage technologies. In doing so, emphasis is given on mature, bulk energy storage technologies, able to support energy management applications. These include pumped hydro storage, compressed air energy storage and battery technologies. Energy storage applications/dispatch strategies examined are divided into three main categories that focus on private actors, autonomous electricity grids and utility-scale systems. For private energy storage actors, active, profit-seeking participation in energy markets is examined through the evaluation of high-risk arbitrage strategies. Furthermore, the interplay of energy storage and demand side management (DSM) is studied for private actors exposed to increased electricity prices and energy insecurity, designating also the potential for combined strategies of arbitrage and DSM. To reduce the investment risks associated with participation in energy markets, a novel aspect of collaboration between energy storage and RES is accordingly investigated for energy storage investors, proposing the use of storage for the delivery of guaranteed RES power during peak demand periods and stimulating the development of state support instruments such as feed-in tariffs. Next, attention is given on the introduction of energy storage systems in autonomous island grids. Such autonomous systems comprise ideal test-benches for energy storage and smart-grids, owed to the technical challenges they present on the one hand (e.g. low levels of energy diversity and limitations in terms of grid balancing capacity) and the high electricity production cost determining the local energy sector on the other (due to the need for oil imports). To this end, combined operation of RES with energy storage could, under the assumption of appreciable RES potential, prove cost-effective in comparison with the current solution of expensive, oil-based thermal power generation. Moreover, by considering the limited balancing capacity of such autonomous grids, which dictates the oversizing of the storage components in order to achieve increased energy autonomy, the trade-off between DSM and energy storage is next studied, becoming increasingly important as the quality of RES potential decays. With regards to utility-scale energy storage applications, the potential of bulk energy storage to support base-load RES contribution is investigated, proving in this way that the intermittent characteristics of RES power generation could be eliminated. This implies increased energy security at the level of national grids while also challenging the prospect of grid parity for such energy schemes. Furthermore, the market regulating capacity of utility-scale energy storage is reflected through the examination of different market-efficiency criteria, providing system operators with a valuable asset for the improved operation of electricity markets. Finally, the role of utility-scale energy storage in the optimum management of national electricity trade is investigated, designating the underlying problem of embodied carbon dioxide emissions’ exchange over cross-border transmission and paving the way for the consideration of energy storage aspects in electricity grid planning