What may future electricity markets look like?
2023ArticleJournal paper
P. Pinson
Journal of Modern Power System and Clean Energy 11(3), pp. 698-706 (invited)
Publication year: 2023
Should the organization, design and functioning of electricity markets be taken for granted? Definitely not. While decades of evolution of electricity markets in developed countries made us believe that we may have found the right and future-proof model, the substantially and rapidly evolving context of our power and energy systems is challenging this idea in many ways. Actually, that situation brings both challenges and opportunities. Challenges include accommodation of renewable energy generation, decentralization and support to investment, while opportunities are mainly that advances in technical and social sciences provide us with many more options in terms of future market design. We here take a holistic point of view, by trying to understand where we are coming from with electricity markets and where we may be going. Future electricity markets should be made fit for purpose by considering them as a way to organize and operate a socio-techno-economic system.
Strategic coalition for data pricing in IoT data markets
2023ArticleIn press/Available onlineJournal paper
S. R. Pandey, P. Pinson, P. Popovski
IEEE Internet of Things Journal, in press/available online
Publication year: 2023
This paper establishes a market for trading Internet of Things (IoT) data that is used to train machine learning models. The data, either raw or processed, is supplied to the market platform through a network, and the price of such data is controlled based on the value it brings to the machine learning model under the adversity of the correlation property of data. Eventually, a simplified distributed solution for a data trading mechanism is derived that improves the mutual benefit of devices and the market. Our key proposal is an efficient algorithm for data markets that jointly addresses the challenges of availability and heterogeneity in participation, as well as the transfer of trust and the economic value of data exchange in IoT networks. The proposed approach establishes the data market by reinforcing collaboration opportunities between devices with correlated data to limit information leakage. Therein, we develop a networkwide optimization problem that maximizes the social value of coalition among the IoT devices of similar data types; at the same time, it minimizes the cost due to network externalities, i.e., the impact of information leakage due to data correlation, as well as the opportunity costs. Finally, we reveal the structure of the formulated problem as a distributed coalition game and solve it following the simplified split-and-merge algorithm. Simulation results show the efficacy of our proposed mechanism design toward a trusted IoT data market, with up to 32.72% gain in the average payoff for each seller.
Robust scheduling with purchase of distributed predictive information
2023ArticleJournal paperPreprint
R. Xie, P. Pinson, Y. Chen
preprint, under review
Publication year: 2023
Robust scheduling is an essential way to cope with uncertainty. However, the rising unpredictability in net demand of distributed prosumers and the lack of relevant data make it difficult for the operator to forecast the uncertainty well. This leads to inaccurate, or even infeasible, robust scheduling strategies. In this paper, a novel two-stage robust scheduling model is developed, which enables the operator to purchase predictive information from distributed prosumers to enhance scheduling efficiency. An improved uncertainty set with a smaller variation range is developed by combining the forecasts from the operator and prosumers. Since the improved uncertainty set is influenced by the first-stage information purchase related decisions, the proposed model eventually becomes a case of robust optimization with decision-dependent uncertainty (DDU). An adaptive column-and-constraint generation (C&CG) algorithm is developed to solve the problem within a finite number of iterations. The potential failures of traditional algorithms in detecting feasibility, guaranteeing convergence, and reaching optimal strategies under DDU are successfully circumvented by the proposed algorithm. Case studies demonstrate the effectiveness, necessity, and scalability of the proposed method.
Privacy-preserving convex optimization: When differential privacy meets stochastic programming
2023ArticleJournal paperPreprint
V. Dvorkin, F. Fioretto, P. Van Hentenryck, P. Pinson, J. Kazempour
preprint, under review
Publication year: 2023
Convex optimization finds many real-life applications, where – optimized on real data – optimization results may expose private data attributes (e.g., individual health records, commercial information, etc.), thus leading to privacy breaches. To avoid these breaches and formally guarantee privacy to optimization data owners, we develop a new privacy-preserving perturbation strategy for convex optimization programs by combining stochastic (chance-constrained) programming and differential privacy. Unlike standard noise-additive strategies, which perturb either optimization data or optimization results, we express the optimization variables as functions of the random perturbation using linear decision rules; we then optimize these rules to accommodate the perturbation within the problem’s feasible region by enforcing chance constraints. This way, the perturbation is feasible and makes different, yet adjacent in the sense of a given distance function, optimization datasets statistically similar in randomized optimization results, thereby enabling probabilistic differential privacy guarantees. The chance-constrained optimization additionally internalizes the conditional value-at-risk measure to model the tolerance towards the worst-case realizations of the optimality loss with respect to the non-private solution. We demonstrate the privacy properties of our perturbation strategy analytically and through optimization and machine learning applications.
P2P market coordination methodologies with distribution grid management
2023ArticleJournal paper
A. S. Faria, T. Soares, T. Orlandini, C. Oliveira, T. Sousa, P. Pinson, M. Matos
Sustainable Energy, Grids and Networks 34, art. no. 101075
Publication year: 2023
As prosumers and energy communities gain prominence in power systems, energy trading between prosumers in local P2P markets is paramount. Within this novel market design, peers can directly exchange energy with each other, leading to economic advantages while supporting the decarbonization of the sector. To ensure that voltage and congestion issues are properly addressed, a thorough coordination between the P2P market and the Distribution System Operator is required. This paper presents and compares three mutual-benefit coordination methods. The first method entails applying product differentiation on an iterative basis to avoid exceeding the lines thermal limits, which is performed through penalties on P2P exchanges that may be overloading the network. The second method uses the P2P market with an AC-OPF, ensuring network operation through a flexibility market via upward and downward flexibility. The last one proposes an integrated operation of the P2P market with AC-OPF. All methods are assessed in a typical distribution network with high prosumers integration. The results show that the second method is the one that, fulfilling the network constraints, presents greater social welfare.
Online decision-making for trading wind energy
2023ArticleJournal paper
M. A. Muñoz, P. Pinson, J. Kazempour
Computational Management Science 20, art. no. 33
Publication year: 2023
We propose and develop a new algorithm for trading wind energy in electricity markets, within an online learning and optimization framework. In particular, we combine a component-wise adaptive variant of the gradient descent algorithm with recent advances in the feature-driven newsvendor model. This results in an online offering approach capable of leveraging data-rich environments, while adapting to the nonstationary characteristics of energy generation and electricity markets, also with a minimal computational burden. The performance of our approach is analyzed based on several numerical experiments, showing both better adaptability to nonstationary uncertain parameters and significant economic gains.
On tracking varying bounds when forecasting bounded time series
2023ArticleJournal paperPreprint
A. Pierrot, P. Pinson
preprint, under review
Publication year: 2023
We consider a new framework where a continuous, though bounded, random variable has unobserved bounds that vary over time. In the context of univariate time-series, we look at the bounds as parameters of the distribution of the bounded random variable. We introduce an extended log-likelihood estimation and design algorithms to track the bound through online maximum likelihood estimation. Since the resulting optimization problem is not convex, we make use of recent theoretical results on Normalized Gradient Descent (NGD) for quasi-convex optimization, to eventually derive an Online Normalized Gradient Descent algorithm. We illustrate and discuss the workings of our approach based on both simulation studies and a real-world wind power forecasting problem.
On the efficiency of energy markets with non-merchant storage
2023ArticleJournal paperPreprint
L. Frölke, E. Prat, P. Pinson, R. M. Lusby, J. Kazempour
preprint, under review
Publication year: 2023
Energy market designs with non-merchant storage have been proposed in recent years, with the aim of achieving optimal integration of storage. In order to handle the time linking constraints that are introduced in such markets, existing works commonly make simplifying assumptions about the end-of-horizon storage level. This work analyses market properties under such assumptions, as well as in their absence. We find that, although they ensure cost recovery for all market participants, these assumptions generally lead to market inefficiencies. Therefore we consider the design of markets with non-merchant storage without such simplifying assumptions. Using an illustrative example, as well as detailed proofs, we provide conditions under which market prices in subsequent market horizons fail to reflect the value of stored energy. We show that this problem is essential to address in order to preserve market efficiency and cost recovery. Finally, we propose a method for restoring these market properties in a perfect-foresight setting.
On machine learning-based techniques for future sustainable and resilient energy systems
2023ArticleJournal paper
J. Wang, P. Pinson, S. Chatzivasileiadis, M. Panteli, G. Strbac, V. Terzija
IEEE Transactions on Sustainable Energy 14(2), pp. 1230-1243
Publication year: 2023
Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighbouring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified.
Moving from linear to conic markets for electricity
2023ArticleJournal paper
A. Ratha, P. Pinson, H. Le Cadre, A. Virag, J. Kazempour
European Journal of Operational Research 309(2), pp. 762-783
Publication year: 2023
We propose a new forward electricity market framework that admits heterogeneous market participants with second-order cone strategy sets, who accurately express the nonlinearities in their costs and constraints through conic bids, and a network operator facing conic operational constraints. In contrast to the prevalent linear-programming-based electricity markets, we highlight how the inclusion of second-order cone constraints improves uncertainty-, asset-, and network-awareness of the market, which is key to the successful transition towards an electricity system based on weather-dependent renewable energy sources. We analyze our general market-clearing proposal using conic duality theory to derive efficient spatially-differentiated prices for the multiple commodities, comprised of energy and flexibility services. Under the assumption of perfect competition, we prove the equivalence of the centrally-solved market-clearing optimization problem to a competitive spatial price equilibrium involving a set of rational and self-interested participants and a price setter. Finally, under common assumptions, we prove that moving towards conic markets does not incur the loss of desirable economic properties of markets, namely market efficiency, cost recovery, and revenue adequacy. Our numerical studies focus on the specific use case of uncertainty-aware market design and demonstrate that the proposed conic market brings advantages over existing alternatives within the linear programming market framework.
Fair and scalable electric vehicle charging under electrical grid constraints
2023ArticleIn press/Available onlineJournal paper
G. Tsaousoglou, J. S. Giraldo, P. Pinson, N. G. Paterakis
IEEE Transactions on Intelligent Transportation Systems, in press/available online
Publication year: 2023
The increasing penetration of electric vehicles brings a consequent increase in charging facilities in the low-voltage electricity network. Serving all charging requests on-demand can endanger the safety of the electrical power distribution network. This creates the issue of fairly allocating the charging energy among electric vehicles while maintaining the system within safe operational margins. However, calculating efficient charging schedules for the charging stations bears a high computational burden due to the non-convexities of charging stations’ models. In this paper, we consider a tri-level system with electric vehicles, charging stations, and a power distribution system operator. The objective of each station is formulated as a max-min fairness, mixed-integer linear optimisation problem, while the network constraints are modelled using a second-order conic formulation. In order to tackle the computational complexity of the problem, we decompose it and use a novel approximation method tailored to this problem. We compare the performance of the proposed method with that of the popular alternating direction method of multipliers. Our simulation results indicate that the proposed method achieves a near-optimal solution along with promising scalability properties.
Distributionally robust trading strategies for renewable energy producers
2023ArticleJournal paper
P. Pinson
IEEE Transactions on Energy Markets, Policy and Regulation 1(1), pp. 37-47
Publication year: 2023
Renewable energy generation is 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 consistent with that for 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. Both versions of the distributionally robust Bernoulli newsvendor problem 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.
Data is missing again -- Reconstruction of power generation data using k-Nearest Neighbors and spectral graph theory
2023ArticlePreprint
A. Pierrot, P. Pinson
preprint, under review
Publication year: 2023
The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing nearest-neighbour imputation. Our method relies on learning Laplacian eigenmaps out of the graph of the wind farm through spectral graph theory. These learned representations can be based on the wind farm layout only, or additionally account for information provided by collected data. The related weighted graph is allowed to change with time and can be tracked in an online fashion. Application to the Westermost Rough offshore wind farm shows significant improvement over approaches that do not account for the wind farm layout information.
Chance-constrained economic dispatch of generic energy storage under decision-dependent uncertainty
2023ArticleJournal paper
N. Qi, P. Pinson, M. R. Almassalkhi, L. Cheng, Y. Zhuang
IEEE Transactions on Sustainable Energy 14(4), pp. 2234-2248
Publication year: 2023
Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations.While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible,GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units.
Wind energy forecasting with missing values within a fully conditional specification framework
2022ArticleIn press/Available onlineJournal paper
H. Wen, P. Pinson, J. Gu, Z. Jin
International Journal of Forecasting, in press/available online
Publication year: 2022
Wind power forecasting is essential to power system operation and electricity markets. As abundant data became available thanks to the deployment of measurement infrastructures and the democratization of meteorological modeling, extensive data-driven approaches have been developed within both point and probabilistic forecasting frameworks. These models usually assume that the dataset at hand is complete and overlook missing value issues that often occur in practice. In contrast to that common approach, we rigorously consider here the wind power forecasting problem in the presence of missing values, by jointly accommodating imputation and forecasting tasks. Our approach allows inferring the joint distribution of input features and target variables at the model estimation stage based on incomplete observations only. We place emphasis on a fully conditional specification method owing to its desirable properties, e.g., being assumption-free when it comes to these joint distributions. Then, at the operational forecasting stage, with available features at hand, one can issue forecasts by implicitly imputing all missing entries. The approach is applicable to both point and probabilistic forecasting, while yielding competitive forecast quality within both simulation and real-world case studies. It confirms that by using a powerful universal imputation method based on fully conditional specification, the proposed universal imputation approach is superior to the common impute-then-predict approach, especially in the context of probabilistic forecasting.
To share or not to share? Alternative views on a future of collaborative forecasting
2022ArticleJournal paper
P. Pinson
Foresight 67(7), pp. 8-15
Publication year: 2022
Distributed data refers to information that flows from different sources and possibly different owners. Getting top value from distributed data requires a paradigm shift towards collaborative forecasting. Alternative frameworks exist to support collaborative forecasting, from collaborative analytics to data markets, and from analytics markets to prediction markets. While we should accept that not all data will be openly shared, rethinking forecasting processes with modern communication, distributed computation, and a market component could yield substantial improvements in forecast quality while unleashing new business models
Regression markets and application to energy forecasting
2022ArticleJournal paper
P. Pinson, L. Han, J. Kazempour
TOP 30, pp. 533–573
Publication year: 2022
Energy forecasting has attracted enormous attention over the last few decades, with novel proposals related to the use of heterogeneous data sources, probabilistic forecasting, online learning, etc. A key aspect that emerged is that learning and forecasting may highly benefit from distributed data, though not only in the geographical sense. That is, various agents collect and own data that may be useful to others. In contrast to recent proposals that look into distributed and privacy-preserving learning (incentive-free), we explore here a framework called regression markets. There, agents aiming to improve their forecasts post a regression task, for which other agents may contribute by sharing their data for their features and get monetarily rewarded for it. The market design is for regression models that are linear in their parameters, and possibly separable, with estimation performed based on either batch or online learning. Both in-sample and out-of-sample aspects are considered, with markets for fitting models in-sample, and then for improving genuine forecasts out-of-sample. Such regression markets rely on recent concepts within interpretability of machine learning approaches and cooperative game theory, with Shapley additive explanations. Besides introducing the market design and proving its desirable properties, application results are shown based on simulation studies (to highlight the salient features of the proposal) and with real-world case studies.