• Energy Research

Xiyu Ren is a DPhil candidate at the Smith School of Enterprise and the Environment, the School of Geography and the Environment, and the Institute for New Economic Thinking at the Oxford Martin School of the University of Oxford. Her research interests lie in energy and environmental economics, particularly in energy modeling, electricity market design, times-series analysis, and environmental policies. Iacopo Savelli is a postdoctoral researcher in applied economics at the GREEN Centre, Bocconi University. He is the PI of the peer-reviewed project “Decarbonising the energy system by incentivising energy storage in the right places,” investigating the role of grid-scale energy storage in decarbonizing the energy system. Previously, he was a postdoc at the University of Edinburgh and the University of Oxford working on energy market design. He holds a PhD in engineering, an MS in finance, and a BS in economics. He taught selected energy economics topics at the University of Oxford and the University of Siena. Thomas Morstyn is associate professor in power systems with the Department of Engineering Science of the University of Oxford, where he leads a research group focused on power system control and energy market design. He is also a tutorial fellow at Hertford College and an honorary fellow at the University of Edinburgh. He is an associate editor of IEEE Transactions on Power Systems and co-chairs the IEEE Power & Energy Society Taskforce on Quantum Computing for Power System Operations. His research focuses on the design of control systems and markets to enable the large-scale integration of distributed power system flexibility.

AbstractReaching net-zero carbon emissions requires large shares of intermittent renewable energy and the electrification of end-use consumption, such as heating, making the future energy system highly dependent on weather variability and climate change. Weather exhibits fluctuations on temporal scales ranging from sub-hourly to yearly while climate variations occur on decadal scales. To investigate the intricate interplay between weather patterns, climate variations, and power systems, we developed a database of time series of wind and solar power generation, hydropower inflow, heating and cooling demand using an internally consistent modeling framework. Here we focused on the European continent and generated country level time series extending between 1940 and 2100. Our database can be used for analyses aimed at understanding and addressing the challenges posed by the evolving energy landscape in the face of deep decarbonization and climate change.

A community microgrid is a microgrid composed of several entities that can share energy among themselves. The members of the community can match their demand and supply through an internal local market with a significant reduction of the exchanges with the main grid. Each participant can benefit from a reduction of its energy costs when the energy available locally is cheaper, from a drop of the energy peak demanded from the main grid, and from the new capability to provide energy reserve at aggregate level. In this paper, we analyze how the changes of the community market model parameters can affect both the community as a whole, and the welfare of each participant. The analysis is performed by varying the main drivers of the community market model, the community and storage fees, and the storage capacity. The numerical results are obtained by using real data based on the MeryGrid project.

This paper proposes a novel tariff scheme and a new optimization framework in order to address the recovery of fixed investment costs in transmission network planning, particularly against rising demand elasticity. At the moment, ex-post network tariffs are utilized in addition to congestion revenues to fully recover network costs, which often leads to over/under fixed cost recovery, thus increasing the investment risk. Furthermore, in the case of agents with elastic market curves, ex-post tariffs can cause several inefficiencies, such as mistrustful bidding to exploit ex-post schemes, imperfect information in applied costs and cleared quantities, and negative surplus for marginal generators and consumers. These problems are exacerbated by the increasing price-elasticity of demand, caused for example by the diffusion of demand response technologies. To address these issues, we design a dynamic ex-ante tariff scheme that explicitly accounts for the effect of tariffs in the longterm network planning problem and in the underlying market clearing process. Using linearization techniques and a novel reformulation of the congestion rent, the long-term network planning problem is reformulated as a single mixed-integer linear problem which returns the combined optimal values of network expansion and associated tariffs, while accounting for price-elastic agents and lumpy investments. The advantages of the proposed approach in terms of cost recovery, market equilibrium and increased social welfare are discussed qualitatively and are validated in numerical case studies.

The rise of Local Energy Markets (LEMs) and increasing local flexibility present a key research question: How do local flexibility and LEM operations impact merchant-regulated transmission investments? This paper introduces a novel tri-level framework to integrate local market dynamics into transmission investment decisions. The framework models the sequential operations of the WSM and LEMs, adhering to their respective network constraints, and includes a regulatory mechanism that incentivizes profit-driven Transmission Companies (Transcos) to make social welfare maximizing investments while accounting for local refinement costs. The tri-level optimization problem is asymptotically approximated by a mixed-integer second-order cone programming problem. Our findings from three case studies reveal that the provision of local flexibility substantially reduces reliance on conventional energy generation supplies. Additionally, transmission investment decisions are influenced by the levels of flexible generation and consumers, while adhering to network constraints. Moreover, the tri-level model enhances Transcos’ awareness of the sequential interactions between the WSM and LEMs, enabling them to make investment strategies that are responsive to the changing dynamics of local markets.

Securing an adequate supply of dispatchable resources is critical for keeping a power system reliable under high penetrations of variable generation. Strategic reserves have been used by a range of jurisdictions to procure investment in additional generation reserves given the missing money problem in energy only market designs. Given the growing flexibility and heterogeneity of load enabled by advancements in distributed resource and control technology, strategic reserve procurement needs to be able to reflect the different preferences of energy consumers. To address this challenge this paper develops an insurance risk mechanism for the procurement of strategic reserves that is adapted to a future with variable generation and flexible demand. The proposed design introduces a central insurance scheme with prudential requirements that align diverse consumer reliability preferences with the financial objectives of an insurer-of-last-resort. We illustrate the benefits of the scheme in (i) differentiating load by usage to enable better management of the system during times of extreme scarcity, (ii) incentivizing incremental investment in generation infrastructure that is aligned with consumer reliability preferences and (iii) improving overall reliability outcomes for consumers.

The European market clearing problem is characterized by a set of heterogeneous orders and rules that force the implementation of heuristic and iterative solving methods. In particular, curtailable block orders and the uniform purchase price (UPP) pose serious difficulties. A block is an order that spans over multiple hours, and can be either fully accepted or fully rejected. The UPP prescribes that all consumers pay a common price, i.e., the UPP, in all the zones, while producers receive zonal prices, which can differ from one zone to another. The market clearing problem in the presence of both the UPP and block orders is a major open issue in the European context. The UPP scheme leads to a non-linear optimization problem involving both primal and dual variables, whereas block orders introduce multi-temporal constraints and binary variables into the problem. As a consequence, the market clearing problem in the presence of both blocks and the UPP can be regarded as a non-linear integer programming problem involving both primal and dual variables with complementary and multi-temporal constraints. The aim of this paper is to present a non-iterative and heuristic-free approach for solving the market clearing problem in the presence of both curtailable block orders and the UPP. The solution is exact, with no approximation up to the level of resolution of current market data. By resorting to an equivalent UPP formulation, the proposed approach results in a mixed-integer linear program, which is built starting from a non-linear integer bilevel programming problem. Numerical results using real market data are reported to show the effectiveness of the proposed approach. The model has been implemented in Python, and the code is freely available on a public repository. 15 pages, 7 figures

The provision of energy flexibility services (such as shifting consumption) to electricity systems is becoming increasingly valuable, and can offer additional income for households. Here, we show how the locational distribution of flexibility impacts its value, and whether this could help reduce deprivation in Great Britain. Geospatial analysis shows that nearly 90 % of people (1.3 million) living in the most deprived areas of Greater London can offer high-value flexibility. This could help improve their economic condition, provided that the adoption of appropriate appliances (such as demand response devices) is incentivised, e.g. through government's spatially targeted incentive schemes. The results show that the provision of flexibility could help reduce deprivation in several regions, including Scotland, Greater London, and Yorkshire. By contrast, other areas such as North and North-East England tend to offer lower-value flexibility, and therefore the benefit would be smaller. A flexibility-adjusted deprivation index is proposed to highlight regions where providing flexibility may most help reduce deprivation.