Electricity transmission: an overview of the current debate

Electricity transmission has emerged as critical for successfully liberalising powermarkets. This paper surveys the issues currently under discussion and provides a framework for the remaining papers in this issue. We conclude that signalling the efficient location of generation investment might require even a competitive LMP system to be complemented with deep connection charges. Although a Europe-wide LMP system is desirable, it appears politically problematic, so an integrated system of market coupling, possibly evolving by voluntary participation, should have high priority. Merchant investors may be able to increase interconnector capacity, although this is not unproblematic and raises new regulatory issues. A key issue that needs further research is how to better incentivize TSOs, especially with respect to cross-border issues

Learning the LMP-Load Coupling From Data: A Support Vector Machine Based Approach

This paper investigates the fundamental coupling between loads and locational marginal prices (LMPs) in security-constrained economic dispatch (SCED). Theoretical analysis based on multi-parametric programming theory points out the unique one-to-one mapping between load and LMP vectors. Such one-to-one mapping is depicted by the concept of system pattern region (SPR) and identifying SPRs is the key to understanding the LMP-load coupling. Built upon the characteristics of SPRs, the SPR identification problem is modeled as a classification problem from a market participant's viewpoint, and a Support Vector Machine based data-driven approach is proposed. It is shown that even without the knowledge of system topology and parameters, the SPRs can be estimated by learning from historical load and price data. Visualization and illustration of the proposed data-driven approach are performed on a 3-bus system as well as the IEEE 118-bus system

Opening the Electricity Market to Renewable Energy: Making Better Use of the Grid

Opening the electricity market to renewable energy sources would create flexibility for the further integration of renewable energy, leading to considerably lower costs and emissions. This requires the electricity markets to be reorganized in three ways. Firstly, most trading, and therefore production decision-making, is completed at least one day prior to electricity production. But it must be possible to make adjustments on shorter timescales, in order to effectively utilize wind forecasts, which are only relatively accurate a few hours ahead of production. Secondly, demand for operating reserve to stabilize the grid varies with the uncertainty of forecasts for wind and other generation. Most power plants can offer operating reserve, but only together with electricity. At present, however, operating reserve is traded separately from electricity, often in long-term contracts. And thirdly, network operators generally compensate market participants for grid constraints. But with around 200 GWs of new wind and solar capacity being built by 2020, grid expansion must be combined with transparent, market-based congestion management. The introduction of an independent system operator offering an integrated platform for short-term power trading using a pricing system that internalises network constraints ("nodal pricing") could meet these conditions, allowing further openings of the power market for renewable electrical energy. Experience in the US and simulations for Europe show that international transmission capacity is up to 30% better utilized, congestion management alone yielding annual savings of 1 - 2 billion euros.Market design, renewable energy, nodal pricing, transmission

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions

A contribution of experimental economics toward characterization of the use of market power in oligopolisitc markets

Despite the numerous researches about imperfect competition, the market power remains difficult to quantify using traditional economics methods. In this paper, we propose an experimental economics design and outline some ways of analysis of its results toward characterization of the use of market power. A simple system with two regions and a limited interconnection transfer capacity allocated by an implicit auction is studied. Depending on the experiments two or three subjects share equitably the production capacity in one region, while the production capacity is equitably shared among 5 subjects leading to a more competitive situation in the second one. In both regions, we observe a market price that is different from the theoretical results allowing a quantification of the use of market power. Results are also analyzed based on a characterization of the subjects' behaviour. Further the impact of subjects' behaviour on the market price evolution is described.experimental economics, market power, electricity markets, oligopolistic markets

Network-constrained models of liberalized electricity markets: the devil is in the details

Numerical models for electricity markets are frequently used to inform and support decisions. How robust are the results? Three research groups used the same, realistic data set for generators, demand and transmission network as input for their numerical models. The results coincide when predicting competitive market results. In the strategic case in which large generators can exercise market power, the predicted prices differed significantly. The results are highly sensitive to assumptions about market design, timing of the market and assumptions about constraints on the rationality of generators. Given the same assumptions the results coincide. We provide a checklist for users to understand the implications of different modelling assumptions.Market power, Electricity, Networks, Numeric models, Model comparison

Shift factor-based SCOPF topology control MIP formulations with substation configurations

Topology control (TC) is an effective tool for managing congestion, contingency events, and overload control. The majority of TC research has focused on line and transformer switching. Substation reconfiguration is an additional TC action, which consists of opening or closing breakers not in series with lines or transformers. Some reconfiguration actions can be simpler to implement than branch opening, seen as a less invasive action. This paper introduces two formulations that incorporate substation reconfiguration with branch opening in a unified TC framework. The first method starts from a topology with all candidate breakers open, and breaker closing is emulated and optimized using virtual transactions. The second method takes the opposite approach, starting from a fully closed topology and optimizing breaker openings. We provide a theoretical framework for both methods and formulate security-constrained shift factor MIP TC formulations that incorporate both breaker and branch switching. By maintaining the shift factor formulation, we take advantage of its compactness, especially in the context of contingency constraints, and by focusing on reconfiguring substations, we hope to provide system operators additional flexibility in their TC decision processes. Simulation results on a subarea of PJM illustrate the application of the two formulations to realistic systems.The work was supported in part by the Advanced Research Projects Agency-Energy, U.S. Department of Energy, under Grant DE-AR0000223 and in part by the U.S. National Science Foundation Emerging Frontiers in Research and Innovation under Grant 1038230. Paper no. TPWRS-01497-2015. (DE-AR0000223 - Advanced Research Projects Agency-Energy, U.S. Department of Energy; 1038230 - U.S. National Science Foundation Emerging Frontiers in Research and Innovation)http://buprimo.hosted.exlibrisgroup.com/primo_library/libweb/action/openurl?date=2017&issue=2&isSerivcesPage=true&spage=1179&dscnt=2&url_ctx_fmt=null&vid=BU&volume=32&institution=bosu&issn=0885-8950&id=doi:10.1109/TPWRS.2016.2574324&dstmp=1522778516872&fromLogin=truePublished versio

A Holistic Approach to Forecasting Wholesale Energy Market Prices

Electricity market price predictions enable energy market participants to shape their consumption or supply while meeting their economic and environmental objectives. By utilizing the basic properties of the supply-demand matching process performed by grid operators, known as Optimal Power Flow (OPF), we develop a methodology to recover energy market's structure and predict the resulting nodal prices by using only publicly available data, specifically grid-wide generation type mix, system load, and historical prices. Our methodology uses the latest advancements in statistical learning to cope with high dimensional and sparse real power grid topologies, as well as scarce, public market data, while exploiting structural characteristics of the underlying OPF mechanism. Rigorous validations using the Southwest Power Pool (SPP) market data reveal a strong correlation between the grid level mix and corresponding market prices, resulting in accurate day-ahead predictions of real time prices. The proposed approach demonstrates remarkable proximity to the state-of-the-art industry benchmark while assuming a fully decentralized, market-participant perspective. Finally, we recognize the limitations of the proposed and other evaluated methodologies in predicting large price spike values.Comment: 14 pages, 14 figures. Accepted for publication in IEEE Transactions on Power System