Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

Prescriptive Analytics in Electricity Markets

Electricity markets are a clear example of a sector in which decision making plays a crucial role in its daily activity. Moreover, uncertainty is intrinsic to electricity markets and affects most of the tasks that agents operating in them must carry out. Many of these tasks involve decisions characterized by low risk and being addressed periodically. In this thesis, we refer to these tasks as iterative decisions. This thesis applies the aforementioned innovative frameworks for decision making under uncertainty using contextual information in iterative decision making tasks faced daily by electricity market agents.Decision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the scientific discipline that studies how to solve mathematical programming problems, can help make more efficient decisions in many of these situations. Particularly relevant, because of their frequency and difficulty, are those decisions affected by uncertainty, i.e., in which some of the parameters that precisely determine the optimization problem are unknown when the decision must be made. Fortunately, the development of information technologies has led to an explosion in the availability of data that can be used to assist decisions affected by uncertainty. However, most of the available historical data do not correspond to the unknown parameter of the problem but originate from other related sources. This subset of data, potentially valuable for obtaining better decisions, is called contextual information. This thesis is framed within a new scientific effort that seeks to exploit the potential of data and, in particular, of contextual information in decision making. To this end, in this thesis, we have developed mathematical frameworks and data-driven optimization models that exploit contextual information to make better decisions in problems characterized by the presence of uncertain parameters

Distributionally robust trading strategies for renewable energy producers

Renewable energy generation is to be offered through electricity markets, quite some time in advance. This then leads to a problem of decision-making under uncertainty, which may be seen as a newsvendor problem. Contrarily to the conventional case for which underage and overage penalties are known, such penalties in the case of electricity markets are unknown, and difficult to estimate. In addition, one is actually only penalized for either overage or underage, not both. Consequently, we look at a slightly different form of a newsvendor problem, for a price-taker participant offering in electricity markets, which we refer to as Bernoulli newsvendor problem. After showing that its solution is similar to the classical newsvendor problem, we then introduce distributionally robust versions, with ambiguity possibly about both the probabilistic forecasts for power generation and the chance of success of the Bernoulli variable. All these distributionally robust Bernoulli newsvendor problems admit closed-form solutions. We finally use simulation studies, as well as a real-world case-study application, to illustrate the workings and benefits from the approach

On the use of MPT to derive optimal RES electricity generation mixes

The use of modern portfolio theory (MPT) is a common practice to derive efficient frontiers and support portfolio decision making in financial markets. Although real projects present different characteristics and technical restrictions, the general objective of the decision maker is the same: to maximize the expected return minimizing the portfolio risk. Long term electricity generation decision making is characterized by high uncertainty, high impact on social welfare and a large set of diversified technologies that may be included in future scenarios. The possibility of applying MPT approach to define efficient electricity generation portfolios is explored in this paper focusing on particular in renewable energy sources (RES technologies). The use of MPT for building RES scenarios is demonstrated for the particular case of Portugal. One year hourly data concerning power output from wind, hydro and solar plants along with the power demand was collected and included in the analysis. Three different approaches were considered for designing the efficient frontiers aiming at maximizing the RES electricity generation, minimizing deviation between the demand and the RES production and minimizing the levelised cost of the RES system. The results demonstrate how this approach can be an effective tool to support decision making but put also in evidence the need to build modified MPT models in order to take into account the technical restrictions of the system.QREN, COMPETE, FCT, under Project FCOMP-01-0124-FEDER-01137

Short-term electric market dynamics and generation company decision-making in new deregulated environment

Under the framework developed in [Sheble, 1999a], this work simulates electric market dynamic using systems theory, decision analysis and decision theory. Activities of Generation Companies (GENCOs), the most active players in electric markets, and their impact on market performances are also examined. Decision-making of GENCOs and interactions between them are studied using decision analysis and decision theory.;The first part of this study studies electric market dynamics: dynamics of electricity price, generation output, and other variables. The problem is examined from the viewpoint of an Independent Contract Administrator (ICA) to simulate market performance and GENCOs\u27 activities in different situations. These situations include various interactions among GENCOs (different expectations for competitors adopted by GENCOs), competition types (quantity competition, price competition, both price and quantity competition), market risk levels (decisions under certainty and uncertainty), and different market organizations (with and without certain market information feedback) in the electric market. Decision-making of GENCOs and interactions between them are modeled as control processes and electric markets are modeled as control systems. The corresponding market dynamics is simulated and market dynamic properties are obtained. Simulation results show that interactions between market participants, as well as market risk levels, competition types, and market organizations, are important to market participant\u27s activities and have significant impact on market performances and properties.;The second part of this study is from GENCOs\u27 viewpoint to develop optimal decision-making strategies and models in short term. First of all, GENCOs decision problem in short term in new deregulated environment is identified as a three-dimension problem: how to make optimal decisions for different time in different geographical markets in different service markets to maximize total gain. Then, a new market-based generation scheduling scheme is proposed to solve this problem. Market rules, competitor\u27s activities, uncertainty in the market, bidding strategies, and short-term generation technical constraints are included in the scheme and analyzed using decision analysis and decision theory. Next, Dynamic Programming (DP) and Stochastic Dynamic Programming (SDP) are adopted to solve the new scheduling problems. Results show that in new environment, GENCOs\u27 optimal generation schedules may be very different from schedules proposed in previous work. (Abstract shortened by UMI.

Design of domestic photovoltaics manufacturing systems under global constraints and uncertainty

As global political discourse is taking place where the need for a cleaner energy mix is constantly highlighted, manufacturing strategies are becoming more relevant. Thus, the photovoltaics system design is a crucial aspect related with the overall sustainability. In fact, various countries are considering the potential to locally manufacture different elements of the photovoltaics (PV) value chain and the strategies to incentivize a local manufacturing base. This paper develops a mathematical programming approach for the optimal design of a PV manufacturing value chain considering diverse criteria linked to economic and environmental performance such as minimum sustainable price, transportation capacity, among others, and considering uncertainty. In addition, the proposed methodology involves the dependence over time of supply chain variables and economic parameters such as inflation, electricity cost, and weighted average cost of capital, to determine the manufacturing system topology under uncertain conditions. Our results highlight the importance of planning models to develop markets policies related to supply chains, production level changes and imposed tariffs all while involving uncertainty in economic parameters, which is an improvement compared to planning models that use deterministic formulations. Finally, the proposed methodology and results can encourage decision-making considering probable variations in different parameters

The Levelised Cost of Frequency Control Ancillary Services in Australia’s National Electricity Market

Over the period 2016-2021 Australia’s National Electricity Market (NEM) experienced an investment supercycle, with 16,000MW of new utility-scale variable renewable plant commitments (and an additional 8,000MW of rooftop solar PV) in a power system with a ratcheted peak demand of 35,000MW. The sharp rise in intermittent asynchronous resources and the disorderly loss of 5,000MW of synchronous coal-fired generation plant placed strains on system security – most visibly represented by the rapid deterioration in the distribution of the power systems’ (50Hz) Frequency. This in turn necessitated material changes to the NEM’s suite of Frequency Control Ancillary Service (FCAS) markets. Utility-scale batteries are ideally suited for FCAS duties, but unlike the wholesale electricity market, there is no forward price curve for Frequency Control Ancillary Services, nor is there any systematic framework for determining equilibrium prices that might otherwise be used for investment decision-making. In this article, we develop an approach for quantifying long run equilibrium prices in the markets for Frequency Control Ancillary Services, with the intended application being to guide the suitability of utility-scale battery investments under conditions of uncertainty and missing forward FCAS markets

Risk averse operation of an electricity plant in an electricity market

New methods are required to make optimal operation decisions for electricity generating and consuming plants in market-based electricity industries. Since wholesale electricity is traded on a spot price basis, both generators and consumers face uncertainty in their future income. Operating plants with inter-temporal links are particularly difficult since operation decisions at one instant affect the available operation decisions after that, and hence affect future income. Operation decision making with a risk-averse attitude is a method to handle uncertainty, however, some form of financial instruments, such as forward contracts, are required to allocate risk. Since electricity markets operate on a discrete time basis, a multi-stage decision making method is required to operate an electricity plant with inter-temporal links. Although risk-averse decision making has been used in other contexts, few attempts have been made to use these techniques for multi-stage problems. In this thesis, a new multi-stage risk-averse decision making algorithm is proposed and applied to make operation and forward contract trading decisions for a plant in an electricity market. In the proposed algorithm, risk aversion is incorporated in sequential decision making using the expected utility method with a von Neumann-Morgenstern utility function. Decisions are taken to maximise the utility of total financial income. Since utility functions have a concave shape, the marginal utility of income diminishes with increased income, giving risk aversion. A solution structure similar to dynamic programming is proposed for the risk management algorithm by introducing a state variable to represent past behaviour. The proposed algorithm is applied to make decisions for electricity plant and market models. Simulation results for different plant models show a clear reduction in financial risk when compared with risk-neutral operation. Any reduction in risk is shown to be sensitive to the decision maker s attitude toward risk used in the algorithm. Simulation results suggest that forward contracts play a major role in minimising risk when starting plants with high start up costs. Forward contracts ensure financial security even under unfavourable market conditions. It is shown that, plants employing a risk-averse attitude which do not commit to start, do so after securing their future financial position using forward contracts. In general, the proposed risk management algorithm shows potential for use in electricity markets

Probabilistic short-term wind power forecasting based on kernel density estimators

International audienceShort-term wind power forecasting tools have been developed for some time. The majority of such tools usually provide single-valued (spot) predictions. Such predictions are however often not adequate when the aim is decision-making under uncertainty. In that case there is a clear requirement by end-users to have additional information on the uncertainty of the predictions for performing efficiently functions such as reserves estimation, unit commitment, trading in electricity markets, a.o. In this paper, we propose a method for producing the complete predictive probability density function (PDF) for each time step of the prediction horizon based on the kernel density estimation technique. The performance of the proposed approach is demonstrated using real data from several wind farms. Comparisons to state-of-the-art methods from both outside and inside the wind power forecasting community are presented illustrating the performances of the proposed method

Market Structure and Equilibrium in a Hydro Dominated Electricity Market

Hydro generation plays an important role in electricity generation, especially in countries like New Zealand where 60 to 65 percent of electricity is generated in the hydro sector. In contrast to other types of electricity generation, for example gas generation, hydro generation has two unique properties: uncertainty regarding future resource availability and the ability to store the nature resource. Although hydro resource is often considered to be ‘free’, the ability to store creates an endogenous hidden marginal cost of water: usage today entails the loss of the ability to be used in future periods. Therefore pricing in a hydro dominated electricity market should be different from the approaches applied in markets that consist of generation methods that use only non-storable resources. This paper introduces a tractable approach to model a hydro dominated electricity market that incorporates inter-temporal decision making. It enables us to compute the equilibrium outcomes and the endogenous hidden marginal cost of water under different market structures