Pierre Pinson

Pierre Pinson
Imperial College London | Imperial · Dyson School of Design Engineering

PhD in Energetics, MSc in Applied Mathematics

About

377
Publications
76,808
Reads
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15,846
Citations
Citations since 2016
197 Research Items
12544 Citations
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201620172018201920202021202205001,0001,5002,0002,500
201620172018201920202021202205001,0001,5002,0002,500
201620172018201920202021202205001,0001,5002,0002,500
Introduction
My work is mainly related to the application of mathematical modeling and decision-making methods to the energy sector. The primary focus of this research is on easing the large scale integration of renewable energies into power systems and electricity markets, based on stochastic process modeling, forecasting, optimization and decision-making under uncertainty. Some of my key achievements relate to methods for probabilistic forecasting (energy generation, electricity prices, etc.), offering strategies in electricity markets, and to the proposal of future electricity markets (e.g., probabilistic, peer-to peer).
Additional affiliations
January 2010 - December 2012
European Center For Medium Range Weather Forecasts
April 2009 - May 2009
University of Washington Seattle
Position
  • Visiting researcher
January 2009 - present
University of Oxford

Publications

Publications (377)
Article
The disturbances from variable and uncertain renewable generation propagate from power systems to natural gas networks, causing gas network operators to adjust gas supply nominations to ensure operational security. To alleviate expensive supply adjustments, we develop control policies to leverage instead the flexibility of linepack – the gas stored...
Article
This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality based on the mismatch between the offer and bid prices. As a result, the market disadvantages either the data s...
Article
Flexibility from low-temperature district heating (LTDH) systems favors the integration of renewable energy in power systems. Flexibility from LTDH systems may be revealed and steered through dynamic pricing for heat. For the purpose of optimal heat pricing, the heat market with variable heat pricing is modeled as a Stackelberg game (i.e., with a b...
Article
Current reserve procurement approaches ignore the stochastic nature of reserve asset availability itself and thus limit the type and volume of reserve offers. This paper develops a reliability-aware probabilistic approach that allows renewable generators and load ensembles to offer reserve capacity with reliability attributes. Offers with low relia...
Preprint
Full-text available
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 analyzes market prop...
Article
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base...
Preprint
Full-text available
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 ro...
Preprint
Full-text available
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...
Preprint
This paper proposes and develops 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...
Article
Taking concrete steps towards a carbon-free society, the Danish Parliament has recently approved the establishment of the world’s first two offshore energy hubs on Bornholm and on an artificial island in the North Sea. Being the two first-of-their-kind projects, several aspects related to the inclusion of these “energy islands” in the current marke...
Article
Full-text available
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life...
Article
Full-text available
Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life...
Preprint
Full-text available
This paper considers a market for Internet of Things (IoT) data that is used to train machine learning models. The data is supplied to the market platform through a network and the price of the data is controlled based on the value it brings to the machine learning model. We explore the correlation property of data in a game-theoretical setting to...
Preprint
Full-text available
We present an architecture to implement a decentralised data market, whereby agents are incentivised to collaborate to crowd-source their data. The architecture is designed to reward data that furthers the market's collective goal, and distributes reward fairly to all those that contribute with their data. This is achieved leveraging the concept of...
Preprint
Full-text available
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base...
Article
Full-text available
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...
Preprint
Full-text available
The ever-increasing interest in the collection of data by advancing technical and social sectors makes it distributed in terms of ownership. Also, the diverse expertise of these owners results in the extraction of varying quality of predictive information. Thus, the platforms for pooling forecasts based on distributed data and heterogeneous predict...
Article
The papers in this special section focus on advances in renewable energy forecasting, predictability, business models, and applications in the power industry. During the last 25 years, research has been conducted for developing renewable energy source (RES) forecasting algorithms, especially for wind and solar energy, seeking an improvement of pred...
Preprint
Full-text available
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 modelling, extensive data-driven approaches have been developed within both point and probabilistic forecasting frameworks. These m...
Preprint
Full-text available
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow~(CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base...
Article
We propose stochastic control policies to cope with uncertain and variable gas extractions in natural gas networks. Given historical gas extraction data, these policies are optimized to produce the real-time control inputs for nodal gas injections and for pressure regulation rates by compressors and valves. We describe the random network state as a...
Preprint
Full-text available
Aggregated and coordinated generic energy storage (GES) resources provide sustainable but uncertain flexibilities for power grid operation and renewable energy integration. To optimally cope with multi-uncertainties, this paper proposes a novel chance-constrained optimization (CCO) model for economic dispatch of GES in the day-ahead energy market....
Article
Full-text available
District heating systems become more distributed with the integration of prosumers, including excess heat producers and active consumers. This calls for suitable heat market mechanisms that optimally integrate these actors, while minimizing and allocating operational costs. We argue for the inclusion of network constraints to ensure network feasibi...
Article
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 neighboring ene...
Article
Full-text available
Current bid formats in pool-based electricity markets are ill-equipped to accommodate the broad range of non-conventional sources of flexibility, such as demand response and interconnected heating , natural gas and water infrastructure networks. To address this issue, this paper introduces the novel price-region bid format to be used in both forwar...
Article
Electricity systems around the world are decarbonizing, driven by reductions in the cost of renewable energy and encouraged by supportive regulatory policy. Electricity market designs are increasingly being tested to ensure that the bulk power system can deliver reliable, cost-effective energy to all consumers.
Preprint
Full-text available
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow~(CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base...
Preprint
We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow~(CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base...
Preprint
Full-text available
Current reserve procurement approaches ignore the stochastic nature of reserve asset availability itself and thus limit the type and volume of reserve offers. This paper develops a reliability-aware probabilistic approach that allows renewable generators to offer reserve capacity with reliability attributes. Offers with low reliability are priced a...
Preprint
Full-text available
This paper proposes a regression market for wind agents to monetize data traded among themselves for wind power forecasting. Existing literature on data markets often treats data disclosure as a binary choice or modulates the data quality based on the mismatch between the offer and bid prices. As a result, the market disadvantages either the data s...
Preprint
Full-text available
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 learn-ing, 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 i...
Preprint
Full-text available
The disturbances from variable and uncertain renewable generation propagate from power systems to natural gas networks, causing gas network operators to adjust gas supply nominations to ensure operational security. To alleviate expensive supply adjustments, we develop control policies to leverage instead the flexibility of linepack -- the gas store...
Article
Full-text available
Current electricity and natural gas markets operate with deter-ministic description of uncertain supply, and in a temporally and sectorally decoupled way. This practice in energy systems is being challenged by the increasing integration of stochastic renewable energy sources. There is a growing need for exchanging operational flexibility among ener...
Article
Participants in electricity markets are becoming more proactive because of the fast development of DERs and DSM, which also boosts the emergence of P2P market mechanisms. Moreover, the market is also required to operate in a real-time scheme in response to changes in generation and load to maintain power balance. Therefore, a practicable real-time...
Article
Owing to the fast deployment of distributed energy resources (DERs) and the further development of demand-side management, small agents in electricity markets are becoming more proactive. This may boost the development of peer-to-peer (P2P) market mechanisms. Meanwhile, since actual load and power generation may substantially deviate from schedules...
Article
The increased penetration of renewable energy sources into existing power systems induces challenges in supply–demand balancing. Demand-side flexibility is seen as an option to accommodate variability and limited predictability from renewable energy generation. Heat pumps at residential level, if well coordinated, can be one of those flexibility so...
Article
Full-text available
Wind power has contributed significantly to the increase in electricity generation, but a decision-making tool capable of dealing with its variability and limited predictability is necessary. For this purpose, a novel self-adaptive approach for kernel recursive least-squares machines named multiple challengers is introduced in this work, which is s...
Article
In modern power systems, distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We model multi...
Preprint
Full-text available
When energy customers schedule loads ahead of time, this information, if acquired by their energy retailer, can improve the retailer's load forecasts. Better forecasts lead to wholesale purchase decisions that are likely to result in lower energy imbalance costs, and thus higher profits for the retailer. Therefore, this paper monetizes the value of...
Article
Full-text available
Although distribution grid customers are obliged to share their consumption data with distribution system operators (DSOs), a possible leakage of this data is often disregarded in operational routines of DSOs. This paper introduces a privacy-preserving optimal power flow (OPF) mechanism for distribution grids that secures customer privacy from unau...
Article
Full-text available
The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fai...
Preprint
Taking concrete steps towards a carbon-free society, the Danish Parliament has recently made an agreement on the establishment of the world's first two offshore energy hubs, one on the island of Bornholm and one on an artificial island in the North Sea. Being the two first-of-their-kind projects, several aspects related to the inclusion of these "e...
Article
Marginal utility functions (MUFs) encapsulate the prosumer willingness to trade energy with other market agents. In large-scale distributed optimization schemes scalability and convergence are crucial, and the assumption of linear MUFs is common. In this work, instead of assuming the shape and coefficients of those functions, a method is proposed t...
Preprint
Full-text available
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 elec...
Article
Data exchange between multiple renewable energy power plant owners can lead to an improvement in forecast skill thanks to the spatio-temporal dependencies in time series data. However, owing to business competitive factors, these different owners might be unwilling to share their data. In order to tackle this privacy issue, this paper formulates a...
Article
Full-text available
In an increasingly decentralized energy system with tight interdependencies with heat and electricity markets, prosumers, who act both as consumers and producers at the interface between the markets, are becoming important operational flexibility providers. Policy-makers and market operators need a better understanding of the motivations and behavi...
Preprint
div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We m...
Preprint
div>In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration of Renewable Energy Sources (RES). In this paper we consider an economic dispatch problem for a community of DERs, where energy management decisions are made online and under uncertainty. We m...
Preprint
div>In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local...
Preprint
Full-text available
div>In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local...
Article
In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local prob...
Article
We develop a two-stage stochastic program for energy and reserve dispatch of a joint power and gas system with a high penetration of renewables. Data-driven distributionally robust chance constraints ensure that there is no load shedding and renewable spillage with high probability. We solve this problem efficiently using conditional value-at-risk...
Article
Increasing digitization of the electric power sector allows to further rethink forecasting problems that are crucial input to decision-making. Among other modern challenges, ensuring coherency of forecasts among various agents and at various aggregation levels has recently attracted attention. A number of reconciliation approaches have been propose...
Preprint
The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fai...
Preprint
The proliferation of distributed energy assets necessitates the provision of flexibility to efficiently operate modern distribution systems. In this paper, we propose a flexibility market through which the DSO may acquire flexibility services from asset aggregators in order to maintain network voltages and currents within safe limits. A max-min fai...
Preprint
Full-text available
There is increasing interest in very short-term and higher-resolution wind power forecasting (from minutes to hours ahead), especially offshore. Statistical methods are of utmost relevance, since weather forecasts cannot be informative for those lead times. Those approaches ought to account for the fact that wind power generation as a stochastic pr...
Article
The large shares of wind power generation in electricity markets motivate higher levels of operating reserves. However, current reserve sizing practices fail to account for important topological aspects that might hinder their deployment, thus resulting in high operating costs. Zonal reserve procurement mitigates such inefficiencies, however, the w...
Preprint
Full-text available
My contributions to this voluminous publication can be found on pp 38-40 "The natural law of growth in competition" and on pp 169-170 "Dealing with logistic forecasts in practice"
Article
Full-text available
The interdependence between electricity and natural gas systems has lately increased due to the wide deployment of gas-fired power plants (GFPPs). Moreover, weather-driven renewables introduce uncertainty in the operation of the integrated energy system, increasing the need for operational flexibility. Recently proposed stochastic dispatch models o...
Article
Using flexibility from the coordination of power and natural gas systems helps with the integration of variable renewable energy in power systems. To include this flexibility into the operational decision-making problem, we propose a distributionally robust chance-constrained co-optimization of power and natural gas systems considering flexibility...