Quasi-Stochastic Electricity Markets

Summary of Contribution This article was inspired by price formation changes recently proposed and implemented in several U.S. wholesale electricity markets. The analysis draws from and contributes to three lines of literature. First, the paper specifies two mechanisms that lead to inefficient and inconsistent prices in real-world markets. Second, the article illustrates the importance of considering uncertainty in evaluating policies for pricing in nonconvex markets and observes that convex hull pricing, sometimes described as an ?ideal? due to its uplift-minimizing property in deterministic analyses, can perform poorly in settings with uncertainty. Lastly, the paper strengthens the theoretical basis for operating reserve demand curves by connecting their parameterization to outcomes expected in efficient stochastic markets.

Don't Mess With Texas Solar

2020 was the year of the unexpected, but one constant in the energy industry remained the exponential growth of solar generation in Texas, which largely continued its expansion as predicted. Electric Reliability Council of Texas’s (“ERCOT”) 2019 State of the Grid Report noted that installed solar generation capacity in ERCOT stood at 2,281 megawatts (MW) at year-end 2019, with over 67,000 MW of further solar capacity under study, exceeding even the amount of wind generation capacity under study. By July 2020, installed capacity of solar generation increased by almost 1 gigawatt (GW) to a total of 3,275 MW, representing approximately 2.2% of generating capacity in ERCOT. Solar accounted for 43% of new installed capacity in 2020, the largest share among generation types. The Solar Energy Industries Association (“SEIA”) ranked Texas fifth among the states in installed solar generation capacity in 2019, but based on its high growth rate, Texas is projected to be second only to California within the next five years. Abundant land and consistent sun make Texas an obvious candidate for significant solar generation investment, but ERCOT’s energy-only market makes solar generation with its nonexistent fuel costs especially competitive. Adjustments to the Operating Reserve Demand Curve in 2019 by the Public Utility Commission of Texas have also increased scarcity pricing and made returns more lucrative for a resource that is at its apex when demand is highest on hot, sunny summer afternoons. As this Article was being finalized for publication, the ramifications to the electric power industry in Texas of Winter Storm Uri are not yet clear. However, a preliminary assessment by Pecan Street highlighted the benefits of solar generation in a such a crisis and may spur further interest both at the generation side and behind-the-meter.

Forecasting emergency medicine reserve demand with a novel decomposition-ensemble methodology

Accurate prediction is a fundamental and leading work of the emergency medicine reserve management. Given that the emergency medicine reserve demand is affected by various factors during the public health events and thus the observed data are composed of different but hard-to-distinguish components, the traditional demand forecasting method is not competent for this case. To bridge this gap, this paper proposes the EMD-ELMAN-ARIMA (ELA) model which first utilizes Empirical Mode Decomposition (EMD) to decompose the original series into various components. The Elman neural network and ARIMA models are employed to forecast the identified components and the final forecast values are generated by integrating the individual component predictions. For the purpose of validation, an empirical study is carried out based on the influenza data of Beijing from 2014 to 2018. The results clearly show the superiority of the proposed ELA algorithm over its two rivals including the ARIMA and ELMAN models.

Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization

In the paper, a proposed particle swarm optimization (PPSO) is implemented for dealing with an economic load dispatch (ELD) problem considering the competitive electric market. The main task of the problem is to determine optimal power generation and optimal reserve generation of available thermal generation units so that total profit of all the units is maximized. In addition, constraints, such as generation limit and reserve limit of each unit, power demand and reserve demand, must be exactly satisfied. PPSO is an improved version of conventional particle swarm optimization (PSO) by combining pseudo gradient method, constriction factor and a newly proposed position update method. On the other hand, in order to support PPSO to reach good results for the considered problem, a new constraint handling method (NCHM) is also proposed for determining maximum reserve generation and correcting reserve generation. Three test systems with 3, 10 and 20 units are employed to evaluate the real performance of PPSO. In addition to the comparisons with previous methods, salp swarm optimization (SSA), modified differential evolution (MDE) and eight other PSO methods are also implemented for comparisons. Through the result comparisons, two main contributions of the study are as follows: (1) NCHM is very effective for PSO methods to reach a high success rate and higher solution quality, (2) PPSO is more effective than other methods. Consequently, NCHM and PPSO are the useful combination for the considered problem.

International reserves and domestic money market disequilibrium

Purpose The purpose of this paper is to examine the likely determinants of the demand for official international reserves (hereafter reserves) for India and China in the long run in a basic buffer stock model. The paper also examines the role of domestic money market disequilibrium in the short-run demand for official reserves for both the countries in a dynamic synthesis model. Design/methodology/approach The study used quarterly data for the time period 1993:Q1–2015:Q4. The long-run model is being estimated by following the Frenkel–Jovanovic (1981) buffer stock model and includes the determinants such as transaction motive variable (GDP or Imports), opportunity cost variable (domestic interest rate), precautionary motive variable (volatility of reserves) and exchange rate. The study also examined the role of domestic money market disequilibrium in addition to the above variables in the short-run reserve demand model. The money market disequilibrium term is expected to be negative and significant in the short run. The study employed autoregressive distributed lag bound testing approach to co-integration and unrestricted error-correction model (UECM) approach developed by Pesaran et al. (2001) for estimating the long-run and short-run models, respectively. Findings The co-integration test suggests the existence of long-run relationship between international reserves and its determinants. In the long run, all the variables are statistically significant with expected sign, except domestic interest rate variable for China. It is also found that, the money market disequilibrium term in the short run is negative and significant which validates that an excessive money demand (supply) induces an inflow (outflow) of international reserves for both India and China with a lag of four quarters. The recursive residual tests (CUSUM and CUSUMSQ) confirm the stability of both long-run and short-run reserve demand models. Practical implications The findings and policy implications of this study may be useful for the policy makers of the similar emerging economies for designing money and currency policies. Originality/value This paper is a comparative study which systematically analyzed the reserve demand behavior of the two emerging economies India and China. The study integrates the domestic money market with the international reserve demand behavior for these two economies.

The Uncertain Bidder Pays Principle and Its Implementation in a Simple Integrated Portfolio-Bidding Energy-Reserve Market Model

One reason for the allocation of reserves in electricity markets is the uncertainty of demand and supply. If the bias of the generation portfolio shifts from controllable generators to renewable sources with significantly higher uncertainty, it is natural to assume that more reserve has to be allocated. The price of reserve allocation in European models is dominantly paid by the independent system operator in the form of long-term paid reserve capacities and reserve demand bids submitted to various reserve markets. However, if we consider a scenario where the significant part of generation is allocated in day-ahead auctions, the power mix is not known in advance, so the required reserves can not be efficiently curtailed for the ratio of renewables. In the current paper we analyze an integrated European-type, portfolio-bidding energy-reserve market model, which aims to (at least partially) put the burden of reserve allocation costs to the uncertain energy bidders who are partially responsible for the amount of reserves needed. The proposed method in addition proposes a more dynamic and adaptive reserve curtailment method compared to the current practice, while it is formulated in a computationally efficient way.