Learning from past bids to participate strategically in day-ahead electricity markets

We consider the process of bidding by electricity suppliers in a day-ahead market context, where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent pricesFirst author draf

Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets

We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing suppliers' bids. Based on the submitted bids, the market operator schedules suppliers to meet demand during each hour and determines hourly market clearing prices. Eventually, this game-theoretic process reaches a Nash equilibrium when no supplier is motivated to modify her bid. However, solving the individual profit maximization problem requires information of rivals' bids, which are typically not available. To address this issue, we develop an inverse optimization approach for estimating rivals' production cost functions given historical market clearing prices and production levels. We then use these functions to bid strategically and compute Nash equilibrium bids. We present numerical experiments illustrating our methodology, showing good agreement between bids based on the estimated production cost functions with the bids based on the true cost functions. We discuss an extension of our approach that takes into account network congestion resulting in location-dependent prices

Developing a simulator for the Greek electricity market

Following the liberalization of the Greek electricity market, the Greek Regulatory Authority for Energy (RAE) undertook the design and implementation of a simulator for the wholesale market and its interactions with the Natural Gas Transportation System. The simulator consists of several interacting modules representing all key market operations and dynamics including (i) day-ahead scheduling based on bids of market participants, (ii) natural gas system constraints, (iii) unplanned variability of loads and available capacity driven either by uncertain stochastic outcomes or deliberate participant schedule deviations, (iv) real time dispatch, and (v) financial settlement of day ahead and real time schedule differences. The modules are integrated into one software package capable of simulating all market dynamics, deliberate or probabilistic, and their interactions across all relevant time scales. The intended use of the simulator is to elaborate on and allow RAE to investigate the impact of participant decision strategies on market outcomes. The ultimate purpose is to evaluate the effectiveness of Market Rules, whether existing or contemplated, in providing incentives for competitive behaviour and in discouraging gaming and market manipulation. This paper describes the development of the simulator relative to the current Greek Electricity Market Design and key contemplated revisions.simulation; regulatory policy; electricity markets; market design;

Computation of Convex Hull prices in electricity markets with non-convexities using Dantzig-Wolfe decomposition

The presence of non-convexities in electricity markets has been an active research area for about two decades. The — inevitable under current marginal cost pricing — problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method — i.e., yielding exact, not approximate, CH prices — with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.Accepted manuscrip

Utility spot pricing study : Wisconsin

Spot pricing covers a range of electric utility pricing structures which relate the marginal costs of electric generation to the prices seen by utility customers. At the shortest time frames prices change every five minutes--the same time frame as used in utility dispatch--longer time frames might include 24-hour updating in which prices are set one day in advance but vary hourly as a function of projected system operating costs. The critical concept is that customers see and respond to marginal rather than average costs. In addition the concept of spot pricing includes a "quality of supply" component by which prices are increased at times in which the system is approaching maximum capacity, thus providing a pricing mechanism to replace or augment rationing.This research project evaluated the potential for spot pricing of industrial customers from the perspective both of the utility and its customers. A prototype Wisconsin (based on the WFPCO system) and its industrial customers was evaluated assuming 1980 demand level and tariff structures. The utility system was simplified to include limited interconnection and exchange of power with. surrounding utilities. The analysis was carried out using an hourly simulation model, ENPRO, to evaluate the marginal operating cost for any hour. The industrial energy demand was adjusted to reflect the price (relative to the present time-of-use pricing system). The simulation was then rerun to calculate the change in revenues (and customer bill) and the amount of consumer surplus generated.A second analysis assumed a 5 percent increase in demand with no increase in capacity. Each analysis was carried out for an assumed low and high industrial response to price changes.In an effort to generalize beyond the Wisconsin data and to evaluate the likely implications of a flexible pricing scheme relative to a utility system with a greater level of oil generation, particularly on the margin, the system capacity of the study utility was altered by substitution of a limited number of coal plants by identical but with higher-fuel cost oil-fired plants. The analyses for the modified utility structure are parallel to those for the standard utility structure discussed above.The results of the analysis showed that the flexible pricing system produced both utility and customer savings. At lower capacity utilization the utility recovered less revenue than it did under the present time-of-use rates. While at higher utilization it recovered more. Under all scenarios tested, consumer surplus benefits were five to ten times greater than were simple fuel savings for the utility. While these results must be evaluated in additional testing of specific customer response patterns, it is significant to note that the ability of the customer to choose his pattern more flexibly holds a significant potential for customers to achieve greater surplus--even if their bill may in fact increase. These results are discussed in detail in the report as are a number of customer bill impact considerations and the issues associated with revenue reconciliation

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

The Value of Distributed Energy Resources (DER) to the Grid: Introductionto the concepts of Marginal Capital Cost and Locational Marginal Value

Distributed Energy Resources (DERs) are argued to be a significant benefit to the electric utility grid. While DERs generate significant benefits to their owners and as well as society, the compensation and operating structure of the distribution system of most utilities is such that DERs result in minimal benefits to the distribution system. As we show, the benefits correctly attributed to the distribution company (the wires company) are a function of what service (real, reactive power) the DER is able to provide, when and where, and at what level of certainty the DER is able to provide the service. We introduce the concepts of Marginal Cost of Capacity (MCC) and Locational Marginal Value (LMV) in the calculation of the value of DERs to the distribution system

Computation of Convex Hull Prices in Electricity Markets with Non-Convexities using Dantzig-Wolfe Decomposition

The presence of non-convexities in electricity markets has been an active research area for about two decades. The -- inevitable under current marginal cost pricing -- problem of guaranteeing that no market participant incurs losses in the day-ahead market is addressed in current practice through make-whole payments a.k.a. uplift. Alternative pricing rules have been studied to deal with this problem. Among them, Convex Hull (CH) prices associated with minimum uplift have attracted significant attention. Several US Independent System Operators (ISOs) have considered CH prices but resorted to approximations, mainly because determining exact CH prices is computationally challenging, while providing little intuition about the price formation rationale. In this paper, we describe the CH price estimation problem by relying on Dantzig-Wolfe decomposition and Column Generation, as a tractable, highly paralellizable, and exact method -- i.e., yielding exact, not approximate, CH prices -- with guaranteed finite convergence. Moreover, the approach provides intuition on the underlying price formation rationale. A test bed of stylized examples provide an exposition of the intuition in the CH price formation. In addition, a realistic ISO dataset is used to support scalability and validate the proof-of-concept.Comment: 11 page

Smart building real time pricing for offering load-side regulation service reserves

Abstract-Provision of Regulation Service (RS) reserves to Power Markets by smart building demand response has attracted attention in recent literature. This paper develops tractable dynamic optimal pricing algorithms for distributed RS reserve provision. It shows monotonicity and convexity properties of the optimal pricing policies and the associated differential cost function. Then, it uses them to propose and implement a modified Least Squares Temporal Differences (LSTD) Actor-Critic algorithm with a bounded and continuous action space. This algorithm solves for the best policy within a pre-specified broad family. In addition, the paper develops a novel Approximate Policy Iteration (API) algorithm and uses it successfully to optimize the parameters of an analytic policy function. Numerical results are obtained to demonstrate and compare the Actor-Critic and Approximate Policy Iteration algorithms, demonstrating that the novel API algorithm outperforms the Bounded LSTD Actor-Critic algorithm in both computational effort and policy minimum cost