A novel bidding method for combined heat and power units in district heating systems

We propose a bidding method for the participation of combined heat and power (CHP) units in the day-ahead electricity market. More specifically, we consider a district heating system where heat can be produced by CHP units or heat-only units, e.g., gas or wood chip boilers. We use a mixed-integer linear program to determine the optimal operation of the portfolio of production units and storages on a daily basis. Based on the optimal production of subsets of units, we can derive the bidding prices and amounts of electricity offered by the CHP units for the day-ahead market. The novelty about our approach is that the prices are derived by iteratively replacing the production of heat-only units through CHP production. This results in an algorithm with a robust bidding strategy that does not increase the system costs even if the bids are not won. We analyze our method on a small realistic test case to illustrate our method and compare it with other bidding strategies from literature, which consider CHP units individually. The analysis shows that considering a portfolio of units in a district heating system and determining bids based on replacement of heat production of other units leads to better results

Commitment and Dispatch of Heat and Power Units via Affinely Adjustable Robust Optimization

The joint management of heat and power systems is believed to be key to the integration of renewables into energy systems with a large penetration of district heating. Determining the day-ahead unit commitment and production schedules for these systems is an optimization problem subject to uncertainty stemming from the unpredictability of demand and prices for heat and electricity. Furthermore, owing to the dynamic features of production and heat storage units as well as to the length and granularity of the optimization horizon (e.g., one whole day with hourly resolution), this problem is in essence a multi-stage one. We propose a formulation based on robust optimization where recourse decisions are approximated as linear or piecewise-linear functions of the uncertain parameters. This approach allows for a rigorous modeling of the uncertainty in multi-stage decision-making without compromising computational tractability. We perform an extensive numerical study based on data from the Copenhagen area in Denmark, which highlights important features of the proposed model. Firstly, we illustrate commitment and dispatch choices that increase conservativeness in the robust optimization approach. Secondly, we appraise the gain obtained by switching from linear to piecewise-linear decision rules within robust optimization. Furthermore, we give directions for selecting the parameters defining the uncertainty set (size, budget) and assess the resulting trade-off between average profit and conservativeness of the solution. Finally, we perform a thorough comparison with competing models based on deterministic optimization and stochastic programming.Comment: 31 page

Systematic categorization of optimization strategies for virtual power plants

Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development

Day-Ahead Offering Strategy In The Market For Concentrating Solar Power Considering Thermoelectric Decoupling By A Compressed Air Energy Storage

Due to limited fossil fuel resources, a growing increase in energy demand and the need to maintain positive environmental effects, concentrating solar power (CSP) plant as a promising technology has driven the world to find new sustainable and competitive methods for energy production. The scheduling capability of a CSP plant equipped with thermal energy storage (TES) surpasses a photovoltaic (PV) unit and augments the sustainability of energy system performance. However, restricting CSP plant application compared to a PV plant due to its high investment is a challenging issue. This paper presents a model to assemble a combined heat and power (CHP) with a CSP plant for enhancing heat utilization and reduce the overall cost of the plant, thus, the CSP benefits proved by researches can be implemented more economically. Moreover, the compressed air energy storage (CAES) is used with a CSP-TES-CHP plant in order that the thermoelectric decoupling of the CHP be facilitated. Therefore, the virtual power plant (VPP) created is a suitable design for large power grids, which can trade heat and electricity in response to the market without restraint by thermoelectric constraint. Furthermore, the day-ahead offering strategy of the VPP is modeled as a mixed integer linear programming (MILP) problem with the goal of maximizing the profit in the market. The simulation results prove the efficiency of the proposed model. The proposed VPP has a 2% increase in profit and a maximum 6% increase in the market electricity price per day compared to the system without CAES

Utilization of Electric Prosumer Flexibility Incentivized by Spot and Balancing Markets

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

Strategic network planning in biomass-based supply chains

Fossil resources are limited and will run short. Moreover, the extensive usage of fossil resources is discussed as a key driver for climate change which means that a changeover in basic economic and ecological thinking is necessary. Especially for energy production, there has to be a movement away from the usage of fossil resources and towards renewable resources like wind, water, sun, or biomass. Within the first part of this work a structured review of recent literature on the long-term, strategic planning of biomass-based supply chains is provided. Therefore, in the first step, the overall research field bioeconomy by means of the various utilization pathways of biomass is structured and the demand-oriented view of supply chain management models and the supply-oriented view of bioeconomy are combined. In the second step, a literature review of operations research models and methods for strategic supply chain planning in biomass-based industries are provided. Thirdly, trends are identified and conclusions about research gaps are drawn. One of the identified research gaps is to make biomass-based supply chains profitable on their own, i.e., without governmental subsidies. Therefore, new optimization models are necessary, which should be as close to reality as possible, by for example considering risks and actual surrounding constraints concerning the legal framework. Within the second part of this work, an approach for strategic optimization of biogas plants considering increased flexibility is developed. Biogas plants can produce their energy flexibly and on-demand if their design is adjusted adequately. In order to achieve a flexibly schedulable biogas plant, the design of this plant has to be adapted to decouple the biogas and electricity production. Therefore, biogas storage possibilities and additional electrical capacity are necessary. The investment decision about the size of the biogas storage and the additional electrical capacity depends on the fluctuation of energy market prices and the availability of governmental subsidies. This work presents an approach supporting investment decisions to increase the flexibility of a biogas plant by installing gas storages and additional electrical capacities under consideration of revenues out of direct marketing at the day-ahead market. In order to support the strategic, long-term investment decisions, an operative plant schedule for the future, considering different plant designs given as investment strategies, using a mixed-integer linear programming (MILP) model in an uncertain environment is optimized. The different designs can be evaluated by calculating the net present value (NPV). Moreover, an analysis concerning current dynamics and uncertainties within spot market prices is executed. Furthermore, the influences concerning the variation of spot market prices compared to the influence of governmental subsidies, in particular, the flexibility premium, are revealed by computational results. Besides, the robustness of the determined solution is analyzed concerning uncertainties. The focus of the third part of the work is to consider variable substrate feeding in the mentioned optimization approach because it is expected that variable substrate feeding and thus a demand-oriented biogas production can influence the optimized plant design. In order to support this extension, an operative plant schedule for the future, considering (non-) linear technical characteristics of the biogas plant and the legal framework is optimized. Therefore, mixed-integer linear programming models with integrated approximation approaches of non-linear parts, representing the biogas production rates, are constructed. Furthermore, the influences of fluctuating spot market prices, governmental subsidies, and biomass feedstock prices on the decisions are analyzed for a fictional case example, which is based on a biogas plant in southern Germany. These numerical experiments show that variable substrate feeding can play a decisive role during the optimization of a biogas plant schedule as part of a long-term design optimization. However, the size of the strategic optimization problem makes the use of a heuristic solution algorithm necessary.Fossile Ressourcen sind begrenzt und werden zur Neige gehen. Darüber hinaus wird über die extensive Nutzung fossiler Ressourcen als wesentlicher Treiber des Klimawandels diskutiert, so dass ein Umdenken in der ökonomischen und ökologischen Grundhaltung notwendig ist. Insbesondere bei der Energieerzeugung muss eine Abkehr von der Nutzung fossiler Ressourcen und eine Ausrichtung auf erneuerbare Ressourcen wie Wind, Wasser, Sonne oder Biomasse erfolgen. Im ersten Teil dieser Arbeit wird ein strukturierter Überblick über die aktuelle Fachliteratur zur langfristigen, strategischen Planung von biomassebasierten Supply Chains gegeben. Dazu wird in einem ersten Schritt das gesamte Forschungsfeld "Bioökonomie" anhand der verschiedenen Nutzungspfade von Biomasse strukturiert und die nachfrageorientierte Sichtweise von Supply Chain Management Modellen und die angebotsorientierte Sichtweise der Bioökonomie zusammengeführt. Im zweiten Schritt wird ein Literaturüberblick über Operations-Research-Modelle und Methoden zur strategischen Supply-Chain-Planung in biomassebasierten Branchen gegeben. Im dritten Schritt werden Trends identifiziert und Schlussfolgerungen über Forschungslücken gezogen. Eine der identifizierten Forschungslücken besteht darin, biomassebasierte Supply Chains selbständig, d.h. ohne staatliche Subventionen, profitabel zu machen. Hierfür sind neue Optimierungsmodelle notwendig, die möglichst realitätsnah sein sollten, indem sie z.B. Risiken und tatsächliche Rahmenbedingungen bezüglich der rechtlichen Vorgaben berücksichtigen. Im zweiten Teil dieser Arbeit wird ein Ansatz zur strategischen Optimierung von Biogasanlagen unter Berücksichtigung einer Flexibilitätserhöhung entwickelt. Biogasanlagen können bei geeigneter Auslegung ihre Energie flexibel und bedarfsgerecht produzieren. Um eine Biogasanlage flexibel planbar zu betreiben, muss das Design dieser Anlage so angepasst werden, dass die Biogas- und Stromproduktion entkoppelt werden. Dazu sind Biogasspeichermöglichkeiten und zusätzliche elektrische Kapazität notwendig. Die Investitionsentscheidung über die Größe des Biogasspeichers und der zusätzlichen elektrischen Kapazität hängt von der Schwankung der Energiemarktpreise und der Verfügbarkeit staatlicher Fördermittel ab. Diese Arbeit stellt einen Ansatz zur Unterstützung von Investitionsentscheidungen zur Erhöhung der Flexibilität einer Biogasanlage durch die Installation von Gasspeichern und zusätzlichen elektrischen Kapazitäten unter Berücksichtigung von Erlösen aus der Direktvermarktung am Day-Ahead-Markt vor. Um die strategischen, langfristigen Investitionsentscheidungen zu unterstützen, wird ein operativer Anlagenfahrplan für die Zukunft unter Berücksichtigung verschiedener Anlagendesigns, die als Investitionsstrategien vorgegeben sind, mit Hilfe eines gemischt-ganzzahligen linearen Optimierungsmodells (MILP), unter Berücksichtigung von Unsicherheit, optimiert. Die verschiedenen Designs können durch die Berechnung des Kapitalwerts (NPV) bewertet werden. Darüber hinaus wird eine Analyse der aktuellen Dynamik und der Unsicherheiten der Spotmarktpreise durchgeführt. Darüber hinaus werden die Einflüsse der Varianz der Spotmarktpreise im Vergleich zum Einfluss staatlicher Subventionen, insbesondere der Flexibilitätsprämie, durch Berechnungsergebnisse aufgezeigt. Außerdem wird die Robustheit der ermittelten Lösung hinsichtlich der Unsicherheiten analysiert. Der Fokus des dritten Teils der Arbeit liegt auf der Berücksichtigung eines variablen Substratmanagements in dem entwickelten Optimierungsansatz, da erwartet wird, dass eine variable Substrateinspeisung und damit eine bedarfsgerechte Biogasproduktion das optimierte Anlagendesign beeinflussen kann. Um diese Erweiterung umzusetzen, wird ein operativer Anlagenfahrplan für die Zukunft unter Berücksichtigung (nicht-) linearer technischer Eigenschaften der Biogasanlage und der gesetzlichen Rahmenbedingungen optimiert. Dazu werden gemischt-ganzzahlige lineare Optimierungsmodelle mit integrierten Approximationsansätzen der nichtlinearen Anteile, welche die Biogasproduktionsraten repräsentieren, konstruiert. Des Weiteren werden die Einflüsse von schwankenden Spotmarktpreisen, staatlichen Förderungen und Biomasse-Rohstoffpreisen auf die Entscheidungen für ein fiktives Fallbeispiel, das auf einer Biogasanlage aus Süddeutschland basiert, analysiert. Die numerischen Experimente zeigen, dass die variable Substrateinspeisung bei der Optimierung des Fahrplans einer Biogasanlage im Rahmen einer langfristigen Anlagenoptimierung eine entscheidende Rolle spielen kann. Die Größe des strategischen Optimierungsproblems macht jedoch den Einsatz eines heuristischen Lösungsalgorithmus notwendig

The role of natural gas in setting electricity prices in Europe

The EU energy and climate policy revolves around enhancing energy security and affordability, while reducing the environmental impacts of energy use. The European energy transition has been at the centre of debate following the post-pandemic surge in power prices in 2021 and the energy crisis following the 2022 Russia-Ukraine war. Understanding the extent to which electricity prices depend on fossil fuel prices (specifically natural gas) is key to guiding the future of energy policy in Europe. To this end, we quantify the role of fossil-fuelled vs. low-carbon electricity generation in setting wholesale electricity prices in each EU-27 country plus Great Britain (GB) and Norway during 2015-2021. We apply econometric analysis and use sub/hourly power system data to estimate the marginal share of each electricity generation type. The results show that fossil fuel-based power plants set electricity prices in Europe at approximately 58% of the time (natural gas 39%) while generating only 34% of electricity (natural gas 18%) a year. The energy transition has made natural gas the main electricity price setter in Europe, with gas determining electricity prices for more than 80% of the hours in 2021 in several countries such as Belgium, GB, Greece, Italy, and the Netherlands. Hence, Europe’s electricity markets are highly exposed to the geopolitical risk of gas supply and natural gas price volatility, and the economic risk of currency exchange