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Challenges to the term financing of standalone storage in energy-only electricity markets relate to the difficulty of obtaining long-tenor contracts given multiple volatile revenue streams. Government and central agency-initiated contracting and procurement of storage has garnered interest as a means of catalysing adoption and learning curve effects, particularly given the required scale and pace of the decarbonisation objective. Given the complexity of storage operations and multiple streams of value, standard contract forms are yet to emerge. While there is flexibility in the design of forward contract arrangements, flow on effects of design on incentive compatibility in dispatch, risk-trading and investment represent a critically important avenue of investigation. This article establishes six principles for government-initiated contracting and examines the incentive compatibility of storage contract designs. We find that that preferences for structural simplicity in contract design could introduce incentive incompatibility without careful consideration of the interactions between storage operations and investment.

The war in Ukraine and the associated 2022 energy crisis has had far-reaching effects with seaborne prices for coal and gas reaching multiples (5-6x) of their historic averages. While Europe was the epicentre, countries as far away as Australia were impacted. As a major exporter of coal and gas, domestic markets are linked to seaborne prices. Consequently, forward prices for 2023 delivery in Australia’s National Electricity Market surged from ~$48 in 2021 to $156/MWh in 2022 at one point peaking at $247/MWh. Household electricity tariffs were set to in-crease by 11% in 2023 and 35% in 2024. In late-2022, the Commonwealth Government intervened by setting fuel price caps of $125/t and $12/GJ for coal and gas, respectively. Given an estimated market heat rate of ~8.2GJ/MWh, forward prices reduced to ~$105/MWh. In this article, price increases before- and after- policy interventions are analysed. 2024 tariff increases after policy intervention are forecast to increase by 16.5% (cf.35%), benefiting all customers. State Government hardship policy remains vitally important, however. Underlying levels of fuel poverty in 2024 are forecast at 12.1% pre-policy, and 6.7% after policy intervention, with State-level hardship policies making the larger (3.2 percentage point) contribution to this result.

Risk management in electricity markets is essential for decision making that involve uncertainty. This article researches the dominant themes and research trends in risk management in electricity markets using descriptive analysis, literature mapping, and data mining techniques. The proposed methodology generates the clusters within the dominant themes and provides a comprehensive view of the main authors, journals, and publications, as well as the main lines of research. The results reveal that the academic production of the subject is increasing and the research trends focused on financial risk management, energy resource management, and that climate coverage mechanisms are of great interest to the scientific community.

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.

The decline of fresh-enrolled students and the increase in the number of dropouts in electrical engineering schools might be related to motivation, engagement and differences in learning preferences. This paper provides a detailed description of the development, application, and evaluation of an online, asynchronous simulation game to teach electricity markets concepts named EMGA. The EMGA aims to introduce students to the short-term electricity market structure, highlighting the importance of forecasting tools for decision-making. The EMGA has been deployed in a Master's course with 27 students. The learning effectiveness was assessed with a survey at the end of the exercise. Questions aiming at experience generation, conceptual understanding, skills development and affective evaluation were enquired. Positive results towards experience generation, conceptual understanding and affective evaluation were obtained. Students felt optimistic about the platform's potential. The main complaints from students were their lack of programming experience and the allocated time for the exercise during the course. Results from this small test, along with previously obtained results, are encouraging and might be of potential use for further developing the EMGA and student's experience.

International audience; Challenging times for European electricity security have recently brought light to the importance of a robust power sector with well-functioning electricity markets. Increased uncertainty disrupts the standard practice of decision-making in energy systems. The increased penetration of Renewable Energy Sources (RES) such as wind and photovoltaic plants adds to this uncertainty due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods. However, decision-makers of the energy sector, especially in high-risk situations, need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, climate change, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of extreme events. Therefore, our research focuses on optimizing understandable symbolic representations of data-driven decision-aid models for human operators in the energy sector.

International audience; The European Horizon 2020 project Smart4RES (http://www.smart4res.eu), which started in 2019 and runs until April 2023, aims at improving modelling and forecasting of weather variables necessary to optimize the integration of weather-dependent renewable energy (RES) production (i.e. wind, solar) into power systems and electricity markets. It gathers experts from several disciplines ranging from meteorology, data science, power systems a.o. It aims to contribute to the pathway towards energy systems with very high RES penetrations by 2030 and beyond.This presentation has a double objective:(1) To present a comprehensive overview in terms of KPI improvements of the final results obtained by the project. These results cover thematic objectives including:Improvement of weather and RES forecasting;Streamlined extraction of optimal value from the data through data sharing, data market places, and novel business models for the data;New data-driven optimization and decision-aid tools for market and grid management applications;Validation of new models in living labs and assessment of forecasting value vs costly remedies to hedge uncertainties (i.e. storage). The results obtained are numerous. Without being exhaustive, they include: improved forecasting of weather variables with focus on extreme situations and also through innovative measuring settings (i.e. a network of sky cameras); A seamless approach to couple outputs from different ensemble numerical weather prediction (NWP) models with different temporal resolutions; Advances from ultra-high resolution NWPs based on Large Eddy Simulation; Approaches for RES production forecasting aiming at efficiently combining highly dimensionally input (various types of satellite images, NWPs, spatially distributed measurements etc.); Seamless probabilistic RES forecasting covering multiple time frames and data inputs; Resilient energy forecasting. In the front of applications methods are proposed to optimally use forecasts for the management of storage systems coupled with renewables, for the optimal trading of renewables in multiple markets and for grid management optimization and dynamic security assessment. Prescriptive analytics and explainable AI methods are proposed to optimize decision making. A cost benefit analysis is performed to assess the contribution of different types of data in forecasting problems.(2) To present hierarchized proposals for future research directions. An international workshop is organized by the project (14/04/2023), where experts are invited to assess where RES predictability stands today and propose research directions for the future. In this presentation we will present the conclusions of this workshop. This will be a useful insight for academics, industrials as well as policy makers in the field.Smart4RES Team:Simon Camal, Georges Kariniotakis, Dennis van der Meer, Konstantinos Parginos, Luka Santosuosso, Akylas Stratigakos [MINES Paris, PSL University, Centre PERSEE, France]; Gregor Giebel, Tuhfe Göçmen, Liyang Han [DTU, Denmark]; Pierre Pinson [Imperial College, UK]; Ricardo Bessa; Carla Goncalves, Rui Sousa [INESC TEC, Portugal]; Ivana Aleksovska, Bastien Alonzo, Marie Cassas, Quentin Libois, Marie-Adèle Magnaldo, Laure Raynaud [Meteo France, France]; Gerwin van Dalum, Gerrit Deen, Daan Houf, Remco Verzijlbergh [Whiffle, The Netherlands]; Matthias Lange, Björn Witha [Energy and Meteo Systems, Germany]; Niklas Blum, Jorge Lezaca, Bijan Nouri, Stefan Wilbert [DLR, Germany]; Maria Ines Marques, Catarina Mendes Martins, Manuel Silva [EDP, Portugal]; Wouter De Boer, Marcel Eijgelaar, Ganesh Sauba [DNV, The Netherlands]; John Karakitsios, Theodoros Konstantinou, Dimitrios Lagos, George Sideratos [NTUA/ICCS, Greece]; Theodora Anastopoulou, Efrosini Korka, Christos Vitellas [DEDDIE, Greece]; Stephanie Petit, Clementine Coujard [Dowel Innovation, France]

Standard practice of decision-making in energy systems relies largely on complex modeling chains to address technical constraints and integrate numerous sources of uncertainty. The increased penetration of Renewable Energy Sources (RES) such as solar and wind plants adds complexity due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods. However, decision-makers of the energy sector need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, weather and meteorological conditions, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of an extreme event. Therefore, our research focuses on understandable representations of data-driven decision-aid models for human operators in the energy sector. In order to enhance the interpretability of the AI models, a technique borrowed from the computer science domain is explored and further developed. Genetic programming and more precisely Symbolic Regression is used to derive a symbolic representation for the data-driven model that can take the form of a single equation. This equation results according to a specific reward function. The optimal solutions are selected naturally mimicking the biological theory of survival of the fittest. The main outcome is the production of symbolic representations of the AI models that require minimum changes when applied to different case studies. In this presentation a real-world use case is considered, to demonstrate the added value of the proposed tools for decision-making when trading the production of wind and solar power plants to the day-ahead market. An annual period of data is considered to train and test the proposed model. The typical modeling chain involves as many as 12 models for forecasting RES production, weather and meteorological conditions, together with stochastic optimization to derive trading decisions. A single AI-based model here replaces this complex chain. Such simplification is a significant enhancement to the modeling chain interpretability and facilitates trust to the human decision-maker. This work is carried out in part in the frame of the European project Smart4RES (Grant No 864337) supported by the H2020 Framework Program and in part in the frame the Marie-Curie COFUND project Ai4theSciences (Grant No 945304)

The increasing limitations in the use of fossil fuels due to their limited availability and pollution have increased the use of renewable energies and storage systems for electricity generation. To achieve the goals of the integration of renewable energy, sizing and management methods for hybrid plants are needed to make investments profitable and attractive in these resources. This work presents an optimization method for the sizing and operation of hybrid plants with storage, choosing the best combination of technologies based on resource availability, installation costs and market prices, maximizing an economic index such as the net present value. One of the main contributions of this work is to reduce the oversizing that occurs in traditional methods through a penalty term for lost energy, encouraging investment in batteries to store excess energy above the point of interconnection (POI). In addition, it is intended to cover gaps such as the operation in coupled markets with different execution periods to maximize the benefits of the investment made and to contemplate different generation alternatives together with storage. The presented method is tested through sizing and operation simulations to demonstrate its potential. The presented method is tested through sizing and operation simulations to demonstrate its potential. In scenario A, the best combination of solar energy, photovoltaic energy and storage, is chosen. In scenario B, it is shown how the curtailment of the oversizing is reduced in some months by more than 5%. In scenario C, for daily operation in coupled markets, it is possible to improve the benefits from 0.7% to 37.04% in the days of the year.

The increase in the prices of fossil fuels and environmental issues are leading to a high investment in wind power and solar photovoltaic all over Europe, reducing its dependence on imported fossil fuels. The European countries started incentive programs for investment in these renewable technologies, which consisted of fixed and market premium feed-in tariffs. These feed-in schemes involve long-term contracts with updated prices over inflation. These incentives highly increase the investment and installation of new renewable capacity in Europe. They lead to high renewable penetrations in power systems but originate a tariff deficit due to the difference between market prices and the tariffs paid to these technologies. End-use consumers pay the tariff deficit on retail tariffs. This work analyzes the market-based remuneration of variable renewable energy considering different support schemes and the role of risk-sharing contracts in mitigating the spot price volatility. It presents models able to negotiate bilateral contracts considering risk management, notably risk attitude and risk sharing, bid establishment, and clause (by-laws) negotiation. Furthermore, to evaluate the economic sustainability of renewable generation in Spain, it presents a study for different 12-year support schemes starting in 2010. The results confirmed that, in the case of using risk-sharing contracts during crisis periods, the incidence of low energy prices (price "cannibalization") decreases, such as the tariff deficit. Furthermore, in the case of high-inflation periods, these contracts hedge against the increase in retail prices, resulting in an economic surplus for consumers. info:eu-repo/semantics/publishedVersion

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

Brunel Research Initiative and Enterprise Fund (BRIEF) Optimal distribution network investment and distribution network operator dispatch under peer-to-peer market.

Nowadays, industrial parks play a significant role in the development of electricity market plans, and can thus provide excellent opportunities for market players to actively participate in various electricity markets. The demand response aggregator (DRA) is a major market player that can take advantage of these opportunities. In restructured electricity markets, identifying the consumption patterns of different classes of consumers can be effective in furthering the goals of the DRA. In previous studies on the self-scheduling of the DRA, consumer behavior has not been considered. Such an approach leads to numerous technical problems in the restructured electricity markets. For this purpose, herein, a practical mechanism is presented for executing the self-scheduling process of the DRA by considering the load disaggregation algorithm. The integration of self-scheduling and load disaggregation processes creates a hierarchical optimization problem. The main aim of the constructed hierarchical structure is to find the optimal self-scheduling of the DRA to consciously participate in the electricity markets by identifying the behavior of different consumers. The proposed structure is implemented and evaluated on the industrial park in Saveh, Iran. The time-of-use (TOU) and reward-based demand response (DR) programs are considered as the tools available for the DRA to trade the DR volumes in the day-ahead and balancing electricity markets. 2007-2012 IEEE. Scopus 2-s2.0-85107208339

Since its first formulation in 1962, the Alternating Current Optimal Power Flow (ACOPF) problem has been one of the most important optimization problems in electric power systems. Its most common interpretation is a minimization of generation costs subject to network flows, generator capacity constraints, line capacity constraints, and bus voltage constraints. The main theoretical barrier to its solution is that the ACOPF is a non-convex optimization problem that consequently falls into the as-yet-unsolved space of NP-hard problems. To overcome this challenge, the literature has offered numerous relaxations and approximations of the ACOPF that result in computationally suboptimal solutions with potentially degraded reliability. While the impact on reliability can be addressed with active control algorithms, energy regulators have estimated that the sub-optimality costs the United States ~ $\$ $ 6–19B per year. Furthermore, and beyond its many applications to electric power system markets and operation, the sustainable energy transition necessitates renewed attention towards the ACOPF. This paper contributes a profit-maximizing security-constrained current-voltage AC optimal power flow (IV-ACOPF) model and globally optimal solution algorithm. More specifically, it features a convex separable objective function that reflects a two-sided electricity market. The constraints are also separable with the exception of a set of linear network flow constraints. Collectively, the constraints enforce generator capacities, thermal line flow limits, voltage magnitudes, power factor limits, and voltage stability. The optimization program is solved using a Newton-Raphson algorithm and numerically demonstrated on the data from a transient stability test case. The theoretical and numerical results confirm the globally optimal solution.

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architectures are also used to develop classification models to predict the price difference bands/ranges. The proposed methods are tested using historical PJM market data, and evaluated using Root Mean Squared Error (RMSE) and other customized performance metrics. Case studies show that both deep learning methods outperform common methods including ARIMA, XGBoost and Support Vector Regression (SVR) methods. More importantly, the deep learning methods can capture the magnitude and timing of price difference spikes. Numerical results show the Seq2Seq model performs particularly well and demonstrates generalization capability to extended forecasting lead time.

This editorial provides an overview of ten scientific articles published as the Special paper selection in Thermal Science. The papers were selected from almost six hundred contributions, presented at the 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2021), held on October 10-15, 2021 in Dubrovnik, Croatia. The topics covered in the Special paper selection include economics of electricity markets, nuclear technology, repowering of the coal-based power plant, hybrid renewable energy system, sustainable biomass handling and conversion, post-combustion emissions control, and efficient cooling technology. The editorial also emphasised the papers recently published in the Special Issues of leading scientific journals dedicated to the series of SDEWES Conferences.

n recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study's findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation. info:eu-repo/semantics/publishedVersion

Electrical energy can be traded in liberalized organized markets or by negotiating private bilateral contracts. Competitive markets are central systems where market players can purchase and sell electrical energy. Bilateral contracting consists typically in a private negotiation of power over several months or years between two parties. Price-based demand response considers the active participation of consumers in electricity markets. Consumers adopt demand response programs when responding to market prices or tariffs, as they change over time. Those tariffs can be proposed by retailers by considering their load shape goals, influencing consumers to change their behavior. Consumers may adopt strategies from two different groups, namely by curtailing energy at times of high prices (e.g., peak and intermediate periods) and rescheduling energy away from those times to other times (shifting). This article considers bilateral contracting in electricity markets with demand response. It investigates how curtailment and shifting affect the energy quantity and energy cost of consumers that adopt a time-of-use tariff involving three block periods (i.e., base, intermediate and peak periods). The results indicate that consumers respond to changes in energy price according to their consumption flexibility, while retailers do not always change energy price in response to consumers' efforts to change their consumption patterns. On average, by considering a 5% consumption reduction in the intermediate and peak periods by a consumer agent, a retailer agent reduces the energy price only by 1.5%. info:eu-repo/semantics/publishedVersion

Το βιβλίο αυτό απευθύνεται σε προπτυχιακούς και μεταπτυχιακούς φοιτητές που διδάσκονται την οικονομική και αξιόπιστη λειτουργία των σύγχρονων συστημάτων ηλεκτρικής ενέργειας (ΣΗΕ) και σε επαγγελματίες μηχανικούς που ασχολούνται με τη μελέτη, σχεδίαση, ανάλυση, λειτουργία, και προμήθεια των ΣΗΕ. Το βιβλίο είναι ιδιαίτερα σημαντικό και επίκαιρο: παρουσιάζει την οικονομική και αξιόπιστη λειτουργία τόσο των παραδοσιακών όσο και κυρίως των σύγχρονων ΣΗΕ, όπως αυτά έχουν διαμορφωθεί με βάση τις πρόσφατες εξελίξεις και αλλαγές που σχετίζονται με την απελευθέρωση της αγοράς ηλεκτρικής ενέργειας, τις τεχνολογίες τηλεπικοινωνιών και πληροφορικής για τη βέλτιστη λειτουργία και έλεγχο των ευφυών ΣΗΕ, και τη διαρκώς αυξανόμενη διείσδυση των ανανεώσιμων πηγών ενέργειας και της διανεμημένης παραγωγής στα ΣΗΕ. Είναι ένα εντελώς νέο βιβλίο που θα εμπλουτίσει σημαντικά το υπάρχον αποθετήριο ΚΑΛΛΙΠΟΣ. Δεν υπάρχει αντίστοιχο βιβλίο στην ελληνική βιβλιογραφία. Η φιλοσοφία του βιβλίου είναι η μάθηση μέσα από την εξάσκηση. Για τον λόγο αυτό, το βιβλίο περιλαμβάνει πολλά λυμένα παραδείγματα. Το βιβλίο μπορεί να χρησιμοποιηθεί τόσο από μη εξοικειωμένους όσο και από προχωρημένους επιστήμονες του κλάδου. Φοιτητές, διδάσκοντες, απόφοιτοι και επαγγελματίες του χώρου των ΣΗΕ μπορούν να βρουν στο βιβλίο αυτό το απαραίτητο υλικό για την κατανόηση των εννοιών που σχετίζονται με: - Λειτουργία ΣΗΕ στο σύγχρονο περιβάλλον της απελευθερωμένης αγοράς ηλεκτρικής ενέργειας και των ευφυών δικτύων. - Μοντέλα και μεθόδους επίλυσης για την οικονομική και αξιόπιστη λειτουργία των σύγχρονων ΣΗΕ. - Ανάλυση ηλεκτρικών φορτίων και μεθόδους πρόβλεψης φορτίου. - Πρόβλεψη οριακής τιμής συστήματος σε περιβάλλον απελευθερωμένης αγοράς. - Θεμελίωση και επίλυση του προβλήματος της βέλτιστης AC και της βέλτιστης DC ροής ισχύος. - Ένταξη μονάδων παραγωγής τόσο για ελαχιστοποίηση του κόστους όσο και για μεγιστοποίηση του κέρδους. - Αξιοπιστία σύγχρονων συστημάτων παραγωγής, μεταφοράς και διανομής ηλεκτρικής ενέργειας.

Electricity needs to be consumed at the very moment of production, leading wholesale prices to fluctuate widely at (sub-)hourly time scales. This article investigates the response of aggregate electricity demand to such price variations. Using wind energy as an instrument, we estimate a significant and robust short-term price elasticity of about -0.05 in Germany and attribute this to industrial consumers. While seemingly modest, our results imply that even with limited exposure to real time prices, short-term demand response facilitates decarbonization of the electricity grid by reducing the need for battery storage or backup fossil power by approximately 8%.

Growing numbers of plug-in electric vehicles in Europe will have an increasing impact on the electricity system. Using the agent-based simulation model PowerACE for ten electricity markets in Central Europe, we analyze how different charging strategies impact price levels and production- as well as consumption-based carbon emissions in France and Germany. The applied smart charging strategies consider spot market prices and/or real-time production from renewable energy sources. While total European carbon emissions do not change significantly in response to the charging strategy due to the comparatively small energy consumption of the electric vehicle fleet, our results show that all smart charging strategies reduce price levels on the spot market and lower total curtailment of renewables. Here, charging processes optimized according to hourly prices have the strongest effect. Furthermore, smart charging strategies reduce electricity purchasing costs for aggregators by about 10% compared to uncontrolled charging. In addition, the strategies allow aggregators to communicate near-zero allocated emissions for charging vehicles. An aggregator's charging strategy expanding classic electricity cost minimization by limiting total national PEV demand to 10% of available electricity production from renewable energy sources leads to the most favorable results in both metrics, purchasing costs and allocated emissions. Finally, aggregators and plug-in electric vehicle owners would benefit from the availability of national, real-time Guarantees of Origin and the respective scarcity signals for renewable production.

The European electricity system enters the winter of 2022-2023 facing challenges such as reduced availability of French nuclear power, lower than average stored hydro energy and possible gas shortages in the power industry. These challenges limit the amount of energy available to the system during the winter and reduce the system's ability to meet peak demand and. This study quantitatively analyses the security of electricity supply in Europe (with a focus on Switzerland) between November 2022 and April 2023 by simulating electricity generation and trading under various supply and climate scenarios. To provide a conservative analysis, we exclude certain measures such as demand reduction targets and fuel switching efforts. The results show that Europe, and France in particular, can avoid transmission congestion and power shortages in the event of reduced French nuclear availability. However, in a cold winter, supply shortages may occur in gas-dependent countries if reduced French nuclear availability is combined with gas generation being limited to historical levels. Furthermore, if a 20% gas saving is enforced, Europe could face supply shortages in various weather realisations. On the other hand, Switzerland faces few challenges even in cold winters with the reduced availability of French nuclear and the implementation of 10% gas savings in the electricity system. This is due to the flexibility of the Swiss hydro system and trading opportunities with neighbouring countries.

Renewable energy installations are rapidly gaining market share due to falling technology costs and supportive policies. Meanwhile, the energy price crisis resulting from the Russian-Ukrainian war has shifted the energy policy debate toward the question of how consumers can benefit more from the low and stable generation costs of renewable electricity. Here we suggest a Renewable Pool ('RE-Pool') under which the government passes the conditions of Contracts-for-Difference on to consumers who thereby benefit from reliably low-cost electricity supply. We assess the effect on financing costs, scale, and system friendliness of wind investments, as well risk hedging for consumers' volume risks and hedging incentives.

Over the last two decades, the rapidly decreasing units costs of solar, wind, and energy storage technologies have launched a fundamental transformation in how electric power is produced, distributed, and consumed. Proliferation of these technologies has effected a shift towards a more decentralized, flexible, and sustainable energy system that can meet the growing demand for energy while reducing greenhouse gas emissions from fossil fuels. The work in this thesis studies two principal themes in this transformation: uncertainty and decentralization. Uncertainty is a key challenge in the modern grid resulting from the weather dependence of variable renewables and volatile loads like electric vehicles distributed throughout the grid. Electricity markets, whose function is to regulate the precise balance of supply and demand across the system, face a pressing need for dispatch mechanisms that account for uncertainty while providing participation incentives for generators and loads. We introduce a framework for multi-stage market dispatch and pricing under a general description of forecast uncertainty that enables system operators to explicitly incorporate uncertainty into market-clearing prices. In related work, we study mechanisms that guarantee feasibility of multi-interval dispatch under robust uncertainty and provide participation incentives for shiftable demand response in forward multi-interval markets. The trend towards a more decentralized energy system stems from the inherent modularity of distributed energy resources (DERs), such as solar and storage, as well as the persistent growth in end-use loads. This evolution presents significant challenges to system operators who typically lack the tools and processes for managing a complex, distributed power system. To fill this gap, we introduce and implement a Microgrid Operating System (OS), a software platform for monitoring, modeling, and optimizing microgrids and distribution systems. The Microgrid OS is a central layer that links DER hardware, such as batteries, solar, and flexible loads, to energy applications like cost minimization, emissions reduction, and wholesale market participation. The core functions it provides are data acquisition and processing, system modeling and learning, and optimization and control. We present key modules of the Microgrid OS in the context of several implementation projects in microgrids, commercial buildings, and distribution networks.

The increasing penetrations of distributed renewable generation lead to the need for Citizen Energy Communities. Citizen Energy Communities may be able to be active market players and solve local imbalances. The liberalization of the electricity sector brought wholesale and retail competition as a natural evolution of electricity markets. In retail competition, retailers and communities compete to sign bilateral contracts with consumers. In wholesale competition, producers, retailers and communities can submit bids to spot markets, where the prices are volatile or sign bilateral contracts, to hedge against spot price volatility. To participate in those markets, communities have to rely on risky consumption forecasts, hours ahead of real-time operation. So, as Balance Responsible Parties they may pay penalties for their real-time imbalances. This paper proposes and tests a new strategic bidding process in spot markets for communities of consumers. The strategic bidding process is composed of a forced forecast methodology for day-ahead and short-run trends for intraday forecasts of consumption. This paper also presents a case study where energy communities submit bids to spot markets to satisfy their members using the strategic bidding process. The results show that bidding at short-term markets leads to lower forecast errors than to long and medium-term markets. Better forecast accuracy leads to higher fulfillment of the community programmed dispatch, resulting in lower imbalances and control reserve needs for the power system balance. Furthermore, by being active market players, energy communities may save around 35% in their electrical energy costs when comparing with retail tariffs. info:eu-repo/semantics/publishedVersion

This paper estimates the price impacts of the unanticipated closure of Hazelwood, a large brown coal power plant (1600 MW) in Victoria, Australia. We measure the total impact of the closure on prices in Australia's National Electricity Market for each half-hour interval, and for each state, 12 months from closure. We also break down the impact into direct and indirect effects and find that the total impact of the closure on prices varies considerably across half-hours. The results vary not only in magnitude and across time, but also in statistical significance. Our estimates suggest an upper bound for the impact on the average half-hourly price of $24.02/MWh 12 months from closure, with a total market impact of $6,527.4 million. When we break down the total impact into direct and indirect effects, we find the latter to be the main driver of our results, through a reduction in the merit-order effect of wind generation.

The establishment of a single European day-ahead market has accomplished the integration of the regional day-ahead markets. However, reserve provision and activation remain an exclusive responsibility of regional operators. This limited spatial coordination and the separated structure hinder the efficient utilization of flexible generation and transmission, since their capacities have to be ex-ante allocated between energy and reserves. To promote reserve exchange, recent work proposed a preemptive model that withdraws a portion of the inter-area transmission capacity available from day-ahead energy for reserves by minimizing the expected system cost. This decision-support tool, formulated as a stochastic bilevel program, respects the current architecture but does not suggest area-specific costs that guarantee sufficient benefits for areas to accept the solution. To this end, we formulate a preemptive model in a framework that allows application of game theory methods to obtain a stable benefit allocation, i.e., an outcome immune to coalitional deviations ensuring willingness of areas to coordinate. We show that benefit allocation mechanisms can be formulated either at the day-ahead or the real-time stages, in order to distribute the expected or the scenario-specific benefits, respectively. For both games, the proposed benefits achieve minimal stability violation, while allowing for a tractable computation with limited queries to the bilevel program. Our case studies, based on an illustrative and a more realistic test case, compare our method with well-studied benefit allocations, namely, the Shapley value and nucleolus, and analyze the factors that drive these allocations (e.g., flexibility, network structure, wind correlations). We show that our method performs better in stability and tractability., Omega (United Kingdom), 113

This report explores the impacts of variable renewable energy (VRE) on power trading in the European wholesale electricity market. The intricate operation of a typical power exchange in Europe is accompanied by an equally complex balancing system. The increasing amount of VRE in the power system, such as wind and solar power, has far-reaching impacts for power traders in both this electricity market and the corresponding balancing system. As a result, the electricity market is evolving in unprecedented ways and new participants are entering the playing field to capitalize on the changing dynamics caused by VRE generators. One novel participant, the virtual power plant (VPP), possesses an advantage over other market participants by aggregating VRE generators with controllable renewable energy generators, like biogas and hydro plants, into one entity. This allows the VPP to both gain access to live VRE production data that larger plants don’t have, which it then utilizes to remotely dispatch various subpools of assets, and to provide balancing services to the grid. Subsequently, VPPs are able to trade VRE and other renewable electricity superiorly on the same spot markets and balancing systems as large central power plants and industrial consumers. The report asserts that VPP traders can excel through means of innovative tactics that exploit predictable market impacts caused by VRE power through a robust understanding of the electricity market Denna rapport undersöker effekterna av variabel förnybar energi (VRE) på krafthandeln på den eu- ropeiska elhandelsmarknaden för stora aktörer. Den komplicerade driften av ett typiskt kraftutbyte i Europa åtföljs av ett lika komplicerat balanseringssystem. Den ökande mängden VRE i kraftsys- temet, såsom vind- och solkraft, har långtgående effekter för krafthandlare på både denna elmarknad och motsvarande balanseringssystem. Som ett resultat utvecklas elmarknaden på enastående sätt och nya deltagare kommer in på spelplanen för att dra nytta av den förändrade dynamiken som orsakas av VRE-generatorer. En ny spelare, det virtuella kraftverket (VPP), har en fördel jämfört med andra mark- nadsaktörer genom att samla VRE-generatorer med styrbara förnybara energiproducenter, som biogas och vattenkraftverk, till en enhet. Detta gör att VPP både kan få tillgång till live VRE-produktionsdata som större anläggningar inte har, som den sedan använder för att distribuera olika underpooler av till- gångar och för att tillhandahålla balanstjänster till nätet. Därefter kan VPP: er handla med VRE och annan förnybar el på ett överlägset sätt på samma spotmarknader och balanseringssystem som stora centrala kraftverk och industrikonsumenter. Rapporten visar att VPP-handlare kan göra vinster genom innovativa handelsstrategier som utnyttjar förutsägbara marknadseffekter orsakade av VRE-kraft genom en detaljerad förståelse för elmarknaden och unik tillgång till data för produktionen av förnybar energi Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.b - Per a 2030, ampliar la infraestructura i millorar la tecnologia per tal d’oferir serveis d’energia moderns i sos­tenibles per a tots els països en desenvolupament, en particular els països menys avançats, els petits estats insulars en desenvolupament i els països en desenvolupament sense litoral, d’acord amb els programes de suport respectius

By 2030, the Australian government intends to increase the market share of electric vehicles by at least 50%. This paper investigates the effect of the upcoming large-scale implementation of plug-in hybrid electric vehicles (PHEVs) in Australia on thermal power generation expansion decisions and integration of wind power into New South Wales (NSW), the largest regional market in the Australian National Electricity Market (NEM). Furthermore, seeking to understand whether optimizing and allocating PHEV charging loads can effectively decrease investments and operational costs of the power system with high wind penetration, we also evaluate the potential cost savings from the dedicated governmental charging strategy of PHEV fleets. To this end, we develop a Mixed Integer Linear Programming (MILP) optimization model for optimal power system investment decisions. The simulation results demonstrate that, grid-connected PHEVs obviously increase power system financial and operational burdens. When compared to the scenario with dumb charging strategy, smart charging strategy results in less investment in thermal generation and flatter load curve, as well as less wind curtailment.

There could hardly be any industry that has still not explored blockchain. Believing in how the technology behind bitcoin could be changing money, business, and the world1, blockchain’s impact has been felt everywhere. Blockchain is considered a revolutionary technology in its ability to store information and enable transactions. Since the application of this technology varies with the context, the blockchain technology has taken different forms ranging from its public permissionless network that is open to all to its private permissioned network that is restricted to a few. Not indulging in the debates whether a particular blockchain is really a blockchain or not, this research instead explores blockchain’s features that the stakeholders of some of the most regulated industries find valuable. The main research question that this thesis answers is ‘Why do entities enable or restrict blockchain affordances?’ A general promise of blockchain has been its potential of operating as a trustless system making unnecessary the trust to be placed on centralised (trusted) third parties, intermediaries, brokers etc. This makes blockchain capable of eliminating intermediaries and the transaction costs associated with them. Since, this study focusses on the regulated industries namely electricity and telecommunication industry, a transaction cost analysis is carried out in identifying the stakeholders (the transacting parties and the intermediaries), the ‘play of the game’ i.e. institutional arrangement and the ‘rules of the game’ i.e. institutional environment in carrying out exchanges in the two contexts. However, after reviewing the status quo of the contexts, it can be deduced that the existing systems already reduce certain transaction costs incurred by certain stakeholders of these sectors. These systems are established through regulations for example, the regulatory directives that restrains the behavioural uncertainty of participants, the standardization of the information exchange that reduces the compliance costs of participants and the intermediaries that reduces the transaction costs of the market participants. Moreover, a lack of commercial cases of smart contracts, questions whether the technology could automate intermediary functions and further contribute to reduction in transaction costs. Acknowledging a lack of commercial blockchain usecases, this study takes another approach in understanding blockchain’s value addition by investigating its niche. The study operationalizes transaction cost framework to investigate the very first application of blockchain – the Bitcoin. Concluding blockchain’s uniqueness over other ICT is its consensus mechanism, the study establishes the trust factors that blockchain enables and the digital scarcity that is now possible through blockchain. The study finds that bitcoin does not disintermediate all the intermediaries but circumvents a very specific intermediary, moreover, bitcoin also addresses a very specific trust issue. Examining if these findings are also relevant for the electricity and telecommunication industry, the research carries out a multi-case study. The research investigates the expected/realized benefits of blockchain by the project developers in electricity and telecommunication industry through semi-structured interviews. The research also investigates if blockchain is being differently adopted in these sectors by understanding the blockchain design in these two contexts and analyses the reasons for the same. The main findings of the multi-case study are that the blockchain projects in the electricity and telecommunication industry does not address any existing distrust. With the existing ICT employed for exchanges in these two contexts, the study illuminates on an important aspect that decentralisation is not unique to blockchain and is possible through a non-blockchain distributed infrastructure. Given that even a non-blockchain distributed infrastructure does not aim to disintermediate but to decentralise power of a central entity, the research concludes that blockchain is not about disintermediation. The research finds that blockchain usecases in these two industries share a commonality and that they benefit from blockchain’s immutability feature. The study deduces that the aspect blockchain decentralises is the power of an entity influencing a data change; after the data is recorded on blockchain. Given that the majority of blockchain developments are a permissioned network, this study points out that to gain benefits from its immutability feature, blockchain requires decentralisation to avoid a threat of collusion

U ovom radu dan opis tržišta električne energije i njegovih sudionika, objašnjen je pojam aktivnog kupca kao sudionika na tržištu, te agregatora. Objašnjena je mogućnost vršenja pomoćnih usluga u elektroenergetskom sustavu i način nastupa na tržištu pomoćnih usluga te temeljne karakteristike za mogućnost pružanja pomoćnih usluga. Izvršen je opis fotonaponskih sustava te utjecaj obnovljivih izvora na EES. Opisani su različiti tipovi baterijskih spremnika, te je zasebno opisan Li-ion tip baterijskih spremnika korišten za izradu modela. Opisan je matematički model agregatora umreženih baterijskih spremnika i fotonaponskog sustava za nastup na energetsko i tržište pomoćnih usluga te su prikazani rezultati dobiveni simulacijom modela. This paper describes the power market and its participants, explains the concept of an prosumer as a market participant, and an aggregator. The possibility of performing ancillary services in the electric power system and the way of trading on the market of ancillary services, as well as the basic characteristics for the possibility of providing ancillary services, are explained. Photovoltaic systems are described and the influence of renewable sources on EES. Different types of batteries are described, and the Li-ion type of batteries used to make the model is described separately. The mathematical model of the aggregator of combined battery systems and photovoltaic systems for entry into the power and ancillary services market is described, and the results obtained from the model simulation are presented

We explore the relationship between consumer and producer surplus and optimality gaps in mixed-integer linear programming applied to electricity markets. We analyze a long-run adapted resource mix and introduce flexible demand. This allows for comparison of total producer and consumer surplus achieved across near-optimal solutions under different non-convex pricing models. Results indicate that for pricing models dependent on the primal solution, consumer surplus generally increases (although not monotonically) with increasing optimality and producer surplus correspondingly decreases. Short-run cost recovery is also possible over the operating horizon without make-whole payments for all models examined. These results are highly influenced by scarcity rent from price-setting elastic demand., Electric Power Systems Research, 211, ISSN:0378-7796, ISSN:1873-2046

The decarbonisation of the iron and steel industry (ISI) plays an important role in the European ambition to become climate neutral by 2050. The electrification of manufacturing processes and green hydrogen utilisation could, however, put a strain on existing power systems. The aim of this study is to assess the potential of demand response (DR) in decarbonising the ISI. We thereby look at the hydrogen-based direct reduction of iron with electric arc furnace (H2-DRI-EAF), which is currently regarded as the primary technology to decarbonise the industry but it also is the most power intensive low-carbon technology. A linear programming (LP) model is developed to assess the flexibility potential of an exemplary European steel plant that minimises electricity operating costs while maintaining steel production levels. The largest benefits are achieved when increasing the electrolyser size to operate at the times of lowest electricity prices.

In liberalized markets, consumers can choose their electricity suppliers or be part of an energy community. The problem with communities is that they may not have enough weight to trade in markets, which can be overcome by forming coalitions. Electricity is traded in spot markets or through bilateral contracts involving consumers and suppliers. This paper is devoted to bilateral contracting, modeled as a negotiation process involving an iterative exchange of offers and counter-offers. It focuses on coalitions of energy communities. Specifically, it presents team and single-agent negotiation models, where each consumer has strategies, tactics, and decision models. Coalition agents are equipped with intra-team strategies and decision protocols. It also describes a study of bilateral contracts involving a seller agent and a coalition of energy communities. By allying into a coalition, members of energy communities reduced their average costs for electricity by between 2% (large consumers) and 64% (small consumers) according to their consumption. Their levelized cost reduction was 19%. The results demonstrate the power of coalitions when negotiating bilateral contracts and the benefit of a low-consumption members alliance with larger players.

This paper presents an analysis of the influence of power system deviation prediction on the bidding strategy of renewable generation units in spot markets. The optimal bid that a renewable generator makes is subject to the best possible forecast at the time it submits the energy bid to the spot market, which is usually between 12–36 hours in advance of the time of delivery. With these lead times, renewable generators have to assume a significant volume risk in relation to the difference that may occur between the energy finally delivered and the energy previously committed for their participation in the market, since deviations from the committed energy will be valued at the deviations price. In this sense, the analysis carried out in this work shows that the prices of deviations are highly influenced by the energy needs to be raised or lowered by the system at the time of delivery. In other words, in the event that the deviation of the renewable generator goes against the system, the generator will generally have a higher penalty, having to assume the cost of the energy deviation at a price higher than the spot market price. On the other hand, if the plant’s deviation benefits the system, the penalty will be significantly lower (and sometimes even zero). The proposed analysis methodology develops the formulation of the expected benefit of the plant obtained through its participation in the spot market and subsequent settlement of the deviations. This formulation includes the modeling of the effect of the system deviation on the plant’s profits, which allows to satisfactorily identify the influence of the prediction of this variable on the optimal offer strategy. This methodology has been tested for the case of a wind farm operating in the Spanish market. For this purpose, real data of forecasts and final production of the wind farm have been used, as well as real data of the spot market, prices of the balancing service and real deviation of the system, which has allowed to verify in totally realistic conditions the importance of the prediction of the direction of deviation of the system in the optimal bidding. In this way, it will be possible to establish new optimal bidding strategies that focus efforts on advanced prediction techniques for this variable, which will result in greater benefits for wind power plants for their participation in the energy markets.

Improved end-user engagement is considered to be a key factor in decarbonization efforts towards climate neutral energy systems. While first adopters are already actively seeking ways how to optimize their energy consumption, a true shift in consumer behaviour can only be achieved if the financial benefits are well established and presented. The study aims at estimating the economic performance of end-user engagement in provision of demand response services using air-to-air heat pumps as the underlying technology. The results of the paper help evaluate in a real data setting whether the existing market framework provides sufficient incentives to facilitate end-user participation in the demand response service.

The ongoing transition in energy systems from fossil fuels to renewable sources of energy has resulted in a growing uncertainty in all levels of the energy value chain. It has consequently increased the need for higher integration of flexibility technologies to allow for maintaining the secure and reliable operation of the energy systems. Power-to-Hydrogen/Gas (P2HG) technologies are considered as a source of flexibility in power systems. This paper aims to investigate an economically efficient operation of a P2HG unit that is connected to power, gas, oxygen, hydrogen and water infrastructures and is interacting with the respective markets. To this end, it introduces an optimization model that seeks to maximize the profit of the P2HG from participating in various markets considering price forecast uncertainties. The simulation results demonstrate the effectiveness of the proposed approach in containing the impact of price forecast, in the context of the Dutch resources market environment. A key observation is that providing balancing services to the power system accounts for a major share of the revenue of the P2HG unit. It is concluded that under the assumption made, the P2HG unit can have a positive business case in which market revenues fully cover the investment and operational costs of the unit.

In the last couple of years electricity market landscape has significantly changed. The rising share of distributed generation and consumption increases price volatility and causes additional intermittencies to the grid. Thus, the grid must become more flexible. This paper refers to the role of aggregators as flexibility providers in the new smart grids. Its main objective was to develop an actual aggregator model which aggregates commercial and industrial consumers for providing active demand response. The idea behind it was to identify possibilities and potential challenges for aggregators on partially closed electricity markets. For this purpose, an implementation of an aggregator business model on the market in Croatia was analyzed. The paper consists of several parts. In the first part the principle of operation and value proposition of an aggregator business model are defined. The second part refers to regulatory framework and current market design. In the third part the necessary hardware and software solutions for developing the aggregator model are determined and total investment costs are calculated. The last part specifies the properties of the implemented model and analyses its cost-effectiveness. From the analysis, it can be concluded that, when implementing an aggregator on an electricity market which is not completely open, several aspects should be discussed. The main issue emerges from the current market design and the lack of incentives for prosumers to join the aggregator. A new aggregator business model requires separating the balancing responsibility, setting up a compensating financial transfer mechanism and slightly changing the regulatory framework. However, the aggregator has a positive impact on the market and its implementation is cost effective for the prosumers and for the investors. V zadnjih nekaj letih se trgi z električno energijo bistveno spreminjajo. Naraščajoči delež razpršenih virov povečuje nestanovitnost cen in povzroča dodatne nestabilnosti v omrežju, zato se pojavlja potreba po razvoju pametnih omrežij. Pametno omrežje je nov, bolj prilagodljiv sistem, ki integrira več različnih distribuiranih enot in prilagaja njihovo proizvodnjo in odjem. Ta sistem prenaša ne samo energijo, ampak tudi informacije. Vsi njegovi elementi medsebojno komunicirajo in izmenjujejo podatke, kako bi se lažje prilagodili nepričakovanim spremembam. Agregirani sistemi obnovljivih virov energije so produkti pametnega omrežja. Trend decentralizacije, dekarbonizacije in digitalizacije postavlja agregatorje v center sodobnega elektroenergetskega sistema. Njihova vloga kot aktivni udeleženci trga električne energije je bistvena za integracijo razpršenih virov v omrežje in za aktivno sodelovanje odjemalcev na trgu. Z združevanjem več majhnih, fleksibilnih odjemalcev in proizvajalcev, agregatorji povečujejo izkoristek obnovljive energije in omogočajo vstop majhnih odjemalcev na trg. Razvoj in dostopnost agregatorjev sta močno odvisna od tehničnih pogojev, kot so pametni števci in napredne komunikacijske tehnologije. Še bolj odvisna sta od dizajna trga in zakonodaje, ki morata biti oblikovana tako, da motivirata in spodbujata odjemalce k pridružitvi trgu. Namen tega magistrskega dela je analizirati vpliv agregatorjev na trg električne energije s pomočjo poslovnega agregatorskega modela na Hrvaškem. Vloga agregatorjev na sodobnih trgih električne energije je določena skozi agregiranje več majhnih odjemalcev, ki so pripravljeni prilagajati svoj odjem. Magistrsko delo je sestavljeno iz več delov. V prvem delu je razložen princip delovanja agregatorskih poslovnih modelov. Drugi del se nanaša na trenutno zakonodajo v Evropski Uniji in na Hrvaškem. Tretji del določa potrebno strojno in programsko opremo za implementacijo agregatorja. Zadnji del se nanaša na parametre implementiranega modela in na njegovo ekonomsko upravičenost. Agregatorji so posredniki med končnim odjemalcem in trgom električne energije. Razvijajo rešitve za komercialne ali stanovanjske odjemalce, s katerimi izboljšujejo njihovo energetsko učinkovitost in prispevajo k povečanju fleksibilnosti obstoječega omrežja. Osnovni model agregatorja deluje tako, da integrira več majhnih proizvajalcev in/ali odjemalcev s prilagodljivo proizvodnjo/ odjemom. Cilj agregatorja je ustvariti dobiček s prodajo servisnih storitev prenosnemu ali distributivnemu sistem operaterju. Vsak odjemalec deluje v okvirju bilančne skupine. Upravitelji bilančnih skupin so odgovorni za oskrbo z električno energijo in za korekcijo nestabilnosti, ki jo skupina s svojim delovanjem povzroči v omrežju. Bilančna skupina ali zasebni člani bilančne skupine lahko prevzamejo dodatno vlogo na trgu in postanejo agregatorji. Poleg tega obstaja tudi neodvisni agregator, ki ni povezan z dobaviteljem ali z bilančno skupino. V katero smer se bo agregatorski model razvijal je močno odvisno od zakonodaje in želenih funkcij agregatorja. Pri implementaciji agregatorja podjetje, ki namerava ustvariti agregacijo, zbere neobhodne podatke in ustvari mrežo partnerstev. Agregator in partnerji določijo poslovne pogoje in vzpostavijo sistem. Glavni vir prihodkov podjetje ustvari s prodajo energije na trgu. Ko je osnovni koncept agregatorja postavljen, se ta agregator implementira na trg. Kot prej omenjeno, je bil pri razvoju agregatorskega modela upoštevan koncept trga v Republiki Hrvaški. Enako kot vsi evropski trgi je tudi hrvaški trg sestavljen iz več bilančnih skupin. Vsak član na trgu mora delovati preko bilančne skupine kot navadni član ali kot upravitelj skupine. Vsi upravitelji bilančnih skupin so dolžni podpisati pogodbo s prenosnim operaterjem (HOPS), vsi člani bilančnih skupin pa morajo podpisati člansko pogodbo z bilančno skupino. Vsaka bilančna skupina predlaga organizatorju trga (HROTE) napoved proizvodnje/porabe en dan vnaprej. Po preverjanju skladnosti podatkov HROTE pogodbene načrte pošlje HOPS-u, ki preveri tehnične izvedljivosti predloženih količin. Odgovoren za določanje in zagotavljanje tehničnih pogojev prenosa energije in doseganje ravnotežja v sistemu je HOPS. Po fizični dobavi električne energije mora HOPS ponudnikom bilančnih storitev plačati vsako aktivacijo in še zaračunati tistim, ki so s svojo aktivacijo povzročili neravnovesje. Posledično temu je HOPS tudi odgovoren za ustvarjanje in zagotavljanje rezerv za primarno, sekundarno in terciarno regulacijo frekvence. V primeru zaznane nestabilnosti mora prenosni sistem aktivirati svoje rezerve in zagotoviti ustrezno bilančno energijo za ohranjevanje konstantne frekvence. Za primarno in sekundarno frekvenčno regulacijo so odgovorne male turbine in elektrarne v lasti hrvaškega distribucijskega sistema. Terciarna frekvenčna regulacija, ki se aktivira 15 minut po nastanku neravnovesja, je cilj implementiranega agregatorskega modela. Agregator bo v vsakem času pripravljen ponujati ročno rezervo za povrnitev frekvence HOPS-u. Za implementacijo agregatorskega modela je potrebna ustrezna strojna in programska oprema za zajem in analizo podatkov. Potrebni so krmilniki, senzorji in pametni števci. Potreben je tudi SCADA sistem za akvizicijo podatkov ter programska rešitev za analizo in napoved porabe in proizvodnje. Oprema, ki jo je treba inštalirati na strani odjemalcev, je odvisna od diagrama porabe in od že obstoječe opreme na lokaciji. Na začetku so pametni števci in krmilniki z dodatnimi I/O moduli za pošiljanje podatkov SCADA sistemu zahtevani pri vsakem porabniku, neodvisno od tipa in funkcije porabnika. Za meritev je predvidena strojniška oprema iz Schneider Electrica, na podlagi katere so izračunani investicijski stroški. Za zbiranje podatkov je predvidena GEO SCADA programska oprema. Napredna analiza podatkov in napoved porabe in proizvodnje se izvede s pomočjo avtonomne AutoGrid platforme, ki se lahko uporablja za zmogljive podatkovne analize. AutoGrid je paket rešitev z veliko podatkovno in infrastrukturno plastjo, ki izkorišča fleksibilnost odjemalcev, da bi zagotovila prilagodljivost in fleksibilnost omrežja. Platforma je sestavljena iz več zasebnih modulov. Čeprav ima vsak od teh modulov svoje lastnosti, gre navsezadnje za upravljanje, optimizacijo, združevanje in dispečiranje fleksibilnosti. Najpomembnejši elementi agregatorja so odjemalci. Agregatorski model bo sestavljen izključno iz odjemalcev, ki so pripravljeni svoj odjem spreminjati skozi čas. Pri tej analizi je bilo upoštevanih deset odjemalcev, ki pripadajo komercialnem in industrijskem sektorju. Stanovanjski odjemalci niso bili upoštevani zaradi pomanjkljivosti inštaliranih pametnih števcev in zaradi raznolikosti in nepredvidljivosti diagrama porabe. Komercialni in industrijski sektor imata različen diagram porabe. Diagram porabe komercialnega sektorja po navadi začne rahlo naraščati zjutraj, konico doseže v popoldanskem času, potem pa poraba začne zopet rahlo upadati. Odvisno od tega, ali se gre za dvoizmensko ali enoizmensko delo, imajo industrijski odjemalci različne diagrame porabe. Najlažji za modeliranje so dvoizmenski industrijski pogoni, ker imajo precej konstantno porabo. V tem magistrskem delu je bilo izbranih šest fleksibilnih objektov iz komercialnega sektorja, kot so: - hoteli, - data centri in - tržni center. Glavni produkti za prilagajanje odjema pri komercialnem sektorju pridejo iz sistemov za prezračevanje in klimatizacijo, ki se lahko regulirajo. Pametne naprave in avtomatizirani procesi nudijo dodatne prilagoditvene možnosti in rezerve. Pri industrijskem sektorju je bilo izbranih pet različnih industrijskih pogonov s prilagodljivim odjemom. Eden izmed najbolj zanimivih je pogon za proizvodnje jekla, pri katerem se s pomočjo regulacije hitrosti valjanja lahko vpliva na porabo. Izbrani so pogoni za proizvodnjo: - kamna, - jekla in - stekla. Agregator z odjemalci podpiše dvostransko pogodbo in plačuje njihovo razpoložljivost in energijo, ki jo dejansko zagotovijo z aktivacijo. V začetni fazi so odjemalci dolžni zagotoviti potrebne podatke o porabi in razpoložljivosti ter podroben opis svojih dejavnosti in naprav. Vhodni podatki se uporabljajo za optimizacijo in pridobitev optimalne rešitve za dispečiranje. Energija za uravnoteževanje se prodaja prenosnemu sistemu (HOPS-u) kot standardiziran produkt za ročno rezervo za povrnitev frekvence. Standardiziran produkt mora imeti točno določene parametre, kot so period aktivacije, period deaktivacije, velikost in period razpoložljivosti. Prodajna cena je določena na trgu izravnalnih storitev. V skladu z uredbo Evropske Unije je minimalna količina, ki se lahko prodaja kot produkt za terciarno regulacijo, 1 MW. V inicialni fazi projekta je agregator zasnovan tako, da zagotavlja samo pozitivno fleksibilnost omrežju, oziroma zmanjša svojo porabo v primeru pomanjkanja energije in rezerve gredo v sistem. Na koncu magistrskega dela je bila analizirana še ekonomska upravičenost implementiranega modela. Pri tej analizi s pomočjo metode neto sedanje vrednosti (NSV), metodo na podlagi donosnosti (ISD) in metodo dobe vračanja (DV) je izračunan neto denarni tok, akumulirani denarni tok in povračilna doba investicije. Pozitivna NSV in ISD kažeta, da je opisan agregatorski model samozadosten in izplačljiv. Iz te analize lahko zaključimo, da se pogoji za razvoj agregatorjev na evropskem trgu počasi spreminjajo. Decentralizirana proizvodnja in liberalizacija trga močno spodbujata razvoj agregacijske proizvodnje. V začetni fazi našega projekta so bili stroški in razpoložljiva zmogljivost agregatorja natančno določeni. Obnašanje agregatorja na trgu ni bilo možno popolnoma pravilno simulirati zaradi omejitev razpoložljivih informacij o prejšnjih dogodkih na trgu. Naslednje faze vključujejo podpisovanje pogodb s porabniki ter namestitev pametnih števcev in krmilnikov za zajem podatkov na objektih. Dolgoročen cilj je vključiti čim več odjemalcev in ponujati simetrične in nesimetrične izdelke na trgu, primerne ne samo za ročno, ampak tudi za avtomatsko regulacijo frekvence. Čeprav zakonodaja in regulativa še nista dokončana, bo uvedba tega modela sprožila nove prilagoditve trga in močno spodbudila razpršeno proizvodnjo energije in nadaljnji razvoj pametnih omrežij.

Global warming leads to an increase in extreme weather events that considerably threaten critical infrastructures (e.g., electricity or gas networks). The failure of one critical infrastructure may have far-reaching consequences triggering a “cascade of failures” that affects other critical infrastructures. As extreme weather events usually hit affected regions unexpectedly, flexibility in energy systems is key to sustain resilience and rapidly respond to changing conditions, e.g., failed network lines. Hence, we analyze sector coupling (SC) as one option to increase flexibility and exploit synergies between electricity and gas networks. We investigate SC investments that allow for a bi-directional conversion of gas and electricity in the form of gas-fired power plants or power-to-X technologies and their effect on resilience. We propose a two-level market model where, in the upper-level problem, long-term investments in SC technologies are made in anticipation of short-term market clearing as well as uncertain failures and, in the lower-level problem, SC technologies may be activated. We model extreme weather events as a set of discrete scenarios that pose an external risk to the energy system. In our paper, we derive optimal SC investments representing a trade-off between enhancing system resilience and increasing investments. Moreover, we derive optimal SC responses for selected weather events to guide the operation of SC plants under extreme conditions.

Zugleich: Dissertation, Universität Kassel, 2021

International audience; The Horizon2020 Smart4RES project develops forecasting and optimization solutions for the operation of Renewable Energy Sources (RES) and their application in electricity market trading and grid management. As a follow-up of the Poster presented in 2021, this poster presents the latest solutions developed in Smart4RES that intend to cover a large spectrum of prediction horizons and spatial scales in order to maximize the interest for the wind power industry.At the scale of a wind farm, the fluctuations of wind conditions at very-short-term horizons (seconds to minutes ahead) impose significant variations in the structural load of the turbine and its power output. These variations are challenging for a precise control of wind turbines. The forecasting models developed by Smart4RES make use of new data sources unexploited by traditional models such as 2-beam and 4-beam nacelle-mounted LIDAR. Results obtained on an operating turbine equipped with LIDAR show significant improvement in the forecasting error of both structural load and active power output for the next 20 seconds ahead. At the horizon of the next minutes, the power output of a wind farm is impacted by weather variability but also by wake losses that may vary as a function of turbine curtailment following a system operator request. This is why Smart4RES proposes a dynamic Machine Learning (ML) prediction model based on Transfer Learning which provides adaptive forecast that beat state-of-art approaches including a similar ML model trained only in batch mode. Aggregations of renewable power plants are key players for renewable-based provision of services to the grid and optimized management of distribution grids. In this context, a coherent forecast over the entire hierarchy of the aggregation is essential in order to take decisions that are feasible considering local constraints in the various levels of the hierarchy. A main challenge in such hierarchies is to produce a forecast even if data is missing, which can occur frequently at different periods and levels in the hierarchy. Whereas existing approaches tend to discard periods with missing data, which can drastically reduce the amount of available data for training and the applicability of the forecasting model in real conditions, Smart4RES proposes an end-to-end learning approach that is able to derive coherent and precise hierarchical forecasts even in the presence of missing values at different levels of the hierarchy.

International audience; This paper proposes five market models for the procurementof flexibility by transmission (TSO) and distributionsystem operators (DSOs), based on several TSO-DSO coordinationschemes, including a disjoint distribution, disjoint transmission,common, fragmented, and multi-level market. The properties ofthese models are then analyzed. In particular, the common marketis first proven to be more efficient than the other marketmodels. Then, different methods are proposed to adequately priceTSO/DSO interface flows, when procuring cross-grid flexibility. Itis then shown that, when interface flows are optimally priced, thefragmented and multi-level market solutions converge to those ofthe common market, reaching optimal efficiency. To prevent theneed for any network information sharing among system operatorsin the different coordination schemes, decomposition methodsbased on bi-level programming and the alternating directionmethod of multipliers (ADMM) are proposed. A developed casestudy, considering an interconnected transmission-distribution system,corroborates the mathematical findings by highlighting thegreater efficiency of the common market, the effect of adequateinterface pricing on reducing procurement costs, and the capabilityof the decomposition methods to reach optimal market solutionswith limited information exchange.

International audience; Standard practice of decision-making in energy systems relies largely on complex modeling chains to address technical constraints and integrate numerous sources of uncertainty. The increased penetration of Renewable Energy Sources (RES) such as wind and photovoltaic plants adds complexity due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods.However, decision-makers of the energy sector need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, climate change, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of an extreme event.Therefore, our research focuses on understandable representations of data-driven decision-aid models for human operators in the energy sector. In order to enhance the interpretability of the AI models, a technique borrowed from the computer science domain is explored and further developed. Genetic programming and more precisely Symbolic Regression is used to derive a symbolic representation for the data-driven model that can take the form of a single equation. This equation results according to a specific reward function. The optimal solutions are selected naturally mimicking the biological theory of survival of the fittest.The main outcome is the production of symbolic representations of the AI models that require minimum changes when applied to different case studies. In this presentation a real-world use-case is considered, to demonstrate the added value of the proposed tools for decision making when trading the production of wind and solar power plants to the day-ahead market. An annual period of data is considered to train and test the proposed model. The typical modeling chain involves as many as 12 models for forecasting RES production and market quantities together with stochastic optimization to derive trading decisions. This complex chain is here replaced by a single AI-based model. Such simplification is a significant enhancement to the modeling chain interpretability and facilitates trust to the human decision-maker.This work is carried out in part in the frame of the European project Smart4RES supported by the H2020 Framework Program and in part in the frame the Marie-Curie COFUND project Ai4theSciences

International audience; Spatially aggregating renewable power plants is beneficial when participating in electricity markets. In this context, a substantial number of features is available from various data sources. In machine learning, feature selection is common so as to relieve the curse of dimensionality and avoid overfitting. However, there is no guarantee that the selected features result in reliable forecasts and post-processing can therefore be valuable. In this study, we combine model agnostic feature selection with linear and nonlinear probabilistic forecast combination techniques. Moreover, the filters automatically compute the weights for our analog ensemble (AnEn) forecast model that does not require training. We verify our model chain by generating intra-day forecasts of the aggregated output of 60 wind turbines and 20 photovoltaic power plants using 831 input features in total. We show that automatic feature selection improves forecast accuracy while forecast combination improves reliability.

The liberalization of the retail market of electricity increased the tariff choice of end-use consumers. Retailers compete in the retail market for customers, obtaining private portfolios of end-use consumers to manage. Retailers buy electricity at wholesale markets to feed their customers' demands. They can use spot, derivatives, and bilateral markets to acquire the energy they need. The increasing levels of variable renewable energy sources trading at spot markets, increase the price volatility of these markets. To hedge against the volatility of spot prices, retailers may negotiate standard physical or financial bilateral contracts at derivatives markets. Alternatively, they can also negotiate private bilateral contracts. This article addresses the optimization of the retailers purchasing options, to increase their risk-return ratio from electricity markets, and offer more competitive tariffs to consumers. Considering the risk attitude of retailers, they use a multi-step purchasing model composed of a multi-level risk-return optimization and a decision support system. The article presents an agent-based study considering a retailer with a portfolio of 312 real-world consumers. Risk-seeking and risk-neutral retailers obtained a return up to 38%, less than 7% of the optimal return. However, risk-neutral retailers are subject to four times higher risk in their returns than risk-seeking retailers. The results support the conclusion that wholesale markets of electricity are more favourable to risk-seeking retailers, considering their real returns. info:eu-repo/semantics/publishedVersion

The Japan Electric Power Exchange (JEPX) provides a platform for the trading of electric energy in a manner similar to more traditional financial markets. As the number of market agents increase, there is an increasing need for effective price-forecasting models. Electricity price data are observed to exhibit periods of relatively stable, i.e., low-magnitude, low-variance prices interspersed with periods of higher prices accompanied by larger uncertainty. The price data time series therefore exhibits a temporal non-stationarity characteristic that is difficult to capture with typical time series modeling frameworks. In this paper, we implement models for the occurrence of price spike events where spikes are defined as observing prices above a predefined threshold set here at 25 JPY/kWh. This value corresponds to about the 90th percentile of observed prices during peak trading periods. The price spikes time series is observed to be rare events that occur in clusters. We therefore propose to model the data as a Hawkes process whereby the occurrence of a spike event increases the probability of observing more spikes in the period immediately following a price spike event. We test two variations of the classical Hawkes model: the first variation models the change in the magnitude of the underlying intensity as a function of the magnitude of the price spike while the second variation models the change in the decay rate of the underlying intensity as a function of the magnitude of the price spike. An analysis of the performance of the models based on the mean absolute error (MAE) of the spike occurrence probability, a weighted accuracy index, and the Matthews correlation coefficient (MCC) metrics shows the effectiveness of the variable magnitude variation of the Hawkes model in generating short-term forecasts of the occurrence of price spike events. The modified Hawkes model especially outperforms other candidate models as the length of the forecasting horizon increases.

This paper proposes a novel approach for investigating how energy storage devices (ESD) can affect a profit-maximizing distribution network aggregator’s bidding strategy in day-ahead (DA) electricity markets in the presence of uncertainties. The impacts of random renewable generation and demand on the aggregator’s real-time (RT) energy transactions and on network security parameters (e.g., line loadings and nodal voltages) are modelled using distributionally robust chance constraints (DRCC), which guarantee their validity with high probability as long as the true (but unknown) probability distribution of random variables belongs to an ambiguity set. This paper adopts a data-driven approach for defining the ambiguity set using the first and second order moments of random variables extracted from historical data. The aggregator’s overall profit maximization problem (incorporating ESDs and DRCCs) is reformulated into a convex, deterministic equivalent and solved using the MATLAB-based Disciplined Convex Programming software CVX. Results from simulations performed on an actual 11-kV distribution feeder show that the proposed bidding strategy enables the aggregator to achieve an efficient trade-off between profitability in the DA market and risk exposure in RT. Additionally, the results demonstrate the ability of ESDs to further increase the aggregator’s DA profits (through energy arbitrage) without exposing it to additional risks in RT. The impact of incorporating ESDs on distribution network security parameters is also investigated under different settings of the aggregator’s risk tolerance. Finally, some considerations on computational burden and optimisation model accuracy are presented.

This article considers the introduction of Li-ion batteries in photovoltaic power plants to firm their energy production and analyzes the dependence of their degradation on the structure of the electricity market where the power production is traded. The operation of the batteries is decided as a result of successive optimization problems that benefit from the use of deep-learning-based irradiance forecasting tools with low prediction error, which allows the batteries to keep a small size. In addition, state-of-the-art battery aging models are handled to derive a realistic lifetime prognosis. The simulation results obtained by using real data from three European locations, which have different irradiance patterns and market structures, show how the proposed control strategy makes it possible to decouple the saturation rate of the batteries from the climatic conditions of the plant location. Furthermore, regarding the market structure, the results show that the shorter the energy block and the closer the lead time, the lower is the degradation of the batteries. Funding for open access charge: CRUE-Universitat Jaume I

This work proposes a distribution locational marginal price (DLMP)-based transactive energy (TE) framework for distribution systems with enthusiastic or smart prosumers. The framework uses a multi-agent system (MAS) as the basis on which the proposed TE model, i.e., distribution locational marginal price (DLMP) based TE management system (DTEMS), is implemented. DTEMS uses a novel metric known as the nodal earning component, which is determined by the optimal power flow (OPF) based smart auction mechanism, to schedule the TE transactions optimally among the stakeholders by alleviating the congestion in the distribution system. Based on the individual contributions to the congestion relief, DTEMS ranks the prosumers and loads as most valuable players (MVP) and assigns the energy trading price according to the category of the player. The effectiveness of the proposed TE model is verified by simulating the proposed DTEMS for a modified 33 bus radial distribution system fed with various plug-able energy resources, prosumers, and microgrids.