Ashkan Sadeghi-Mobarakeh

Optimal Bidding in Performance-Based Regulation Markets: An MPEC Analysis With System Dynamics

In this paper, we address the problem of optimal bidding in performance-based regulation markets for a large price-maker regulation resource. Focusing on the case of the California Independent System Operator (ISO), detailed market components are considered, such as regulation capacity payment, regulation mileage payment, performance accuracy adjustment, automatic generation control dispatch, and participation factor. Our analysis also incorporates system dynamics of the regulation resource for different resource types and technologies. In principle, our problem formulation is a mathematical program with equilibrium constraints (MPEC). However, our fundamentally new formulations introduce several new challenges in solving the MPEC problem in the context of performance-based regulation markets that are not previously addressed. In fact, global optimization techniques fail to solve the original nonlinear program due to its complexity. Therefore, we undergo several innovative steps to transform the problem into a mixed-integer linear program which is solved with accuracy, reliability, and computational efficiency. Insightful case studies are presented using data from a California ISO regulation market project.

Distribution Grid Reliability Versus Regulation Market Efficiency: An Analysis Based on Micro-PMU Data

There is a growing interest among power system operators to encourage load resources to offer frequency regulation. Prior studies have evaluated the system-wide benefits of such load resource participation. However, the potential adverse impact of wide scale load resource participation on distribution system performance, in the transient time frame, is often overlooked. Our goal is to address this open problem. We focus on a scenario where load resources offer regulation down service. To obtain realistic results, a distribution feeder in Riverside, CA, USA, is considered, where distribution-level phasor measurement units are used to collect high resolution voltage and current data. We start by developing a novel data-driven approach to analyze transient load behaviors. Subsequently, we model the aggregate load transient profile, in form of a three-phase surge current profile, that could be induced on a distribution feeder once a group of loads responds to a regulation down event. The impact of delay, e.g., due to sensing, communications, and load response, is considered. Distribution grid reliability is analyzed by taking into account the characteristics of the main feeder’s protection system as well as each lateral’s protection system. Both momentary and permanent reliability indexes are calculated. Case studies suggest that it is possible to jeopardize distribution grid reliability if several regulation down load resources are on the same feeder. Depending on various factors with respect to load resources, distribution feeder, and regulation market, there may or may not exist ways to break the trade-off between distribution grid reliability and regulation market efficiency. The construction and analysis of the reliability-efficiency curves would be needed for each feeder.

Strategic Bidding for Producers in Nodal Electricity Markets: A Convex Relaxation Approach

Strategic bidding problems in electricity markets are widely studied in power systems, often by formulating complex bi-level optimization problems that are hard to solve. The state-of-the-art approach to solve such problems is to reformulate them as mixed-integer linear programs (MILPs). However, the computational time of such MILP reformulations grows dramatically, once the network size increases, scheduling horizon increases, or randomness is taken into consideration. In this paper, we take a fundamentally different approach and propose effective and customized convex programming tools to solve the strategic bidding problem for producers in nodal electricity markets. Our approach is inspired by the Schmudgens Positivstellensatz Theorem in semialgebraic geometry; but then we go through several steps based upon both convex optimization and mixed-integer programming that results in obtaining close to optimal bidding solutions, as evidenced by several numerical case studies, besides having a huge advantage on reducing computation time. While the computation time of the state-of-the-art MILP approach grows exponentially when we increase the scheduling horizon or the number of random scenarios, the computation time of our approach increases rather linearly.

Strategic selection of capacity and mileage bids in California ISO performance-based regulation market

A new optimization framework is proposed to operate a large, and price-maker generation firm in a performance-based regulation market. It takes into account various design factors such as the details about the underlying performance-based regulation market rules and Automatic Generation Control (AGC) dispatch mechanisms, as well as the system dynamics of generators, e.g., for the case of a steam-turbine generator. Without loss of generality, our focus is on the California Independent System Operator (CAISO) performance-based regulation market and its two available bidding components, namely regulation capacity bidding and regulation mileage bidding. Case studies are presented to gain insights about the proposed bidding method, its characterizes, and its practical implications.

Distribution grid reliability analysis considering regulation down load resources via micro-PMU data

There is a growing interest by power system operators to encourage load resources to offer frequency regulation. There are several studies that evaluate the system-wide benefits of such load resource participation. However, the current literature often overlooks the potential adverse impact on power distribution feeders. This paper seems to address this open problem. We focus on a scenario where load resources offer regulation down service. We start by developing a novel data-driven approach to use distribution-level μPMU data to analyze transient load behaviours. Subsequently, we model the aggregate load transient profile, in form of an aggregate three-phase surge current profile, that is induced on a distribution feeder once a group of loads responds to a regulation down event. The impact of delay, e.g., due to sensing, communications, and load response, is taken into consideration. Distribution grid reliability is then analyzed based on different characteristics of typical over-current protection relay devices. Both momentary and permanent reliability indices are calculated. Case studies suggest that it is possible to jeopardize power distribution grid reliability if several regulation down load resources are on the same feeder. The probability of failure depends on the distribution grid protection system and the amount and distribution of delay in the regulation aggregation system.

Data mining based on random forest model to predict the California ISO day-ahead market prices

In this paper, an ensemble learning model, namely the random forest (RF) model, is used to predict both the exact values as well as the class labels of 24 hourly prices in the California Independent System Operator (CAISO)s day-ahead electricity market. The focus is on predicting the prices for the Pacific Gas and Company (PG&E) default load aggregation point (DLAP). Several effective features, such as the historical hourly prices at different locations, calender data, and new ancillary service requirements are engineered and the model is trained in order to capture the best relations between the features and the target electricity price variables. Insightful case studies are implemented on the CAISO market data from January 1, 2014 to February 28, 2016. It is observed that the proposed data mining approach provides promising results in both predicting the exact value and in classifying the prices as low, medium and high.

Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads

In this paper, we seek to optimally operate a retailer that, on one side, aggregates a group of price-responsive loads and on the other, submits block-wise demand bids to the day-ahead and real-time markets. Such a retailer/aggregator needs to tackle uncertainty both in customer behavior and wholesale electricity markets. The goal in our design is to maximize the profit for the retailer/aggregator. We derive closed-form solutions for the risk-neutral case and also provide a stochastic optimization framework to efficiently analyze the risk-averse case. In the latter, the price-responsiveness of the load is modeled by means of a non-parametric analysis of experimental random scenarios, allowing for the response model to be non-linear. The price-responsive load models are derived based on the Olympic Peninsula experiment load elasticity data. We benchmark the proposed method using data from the California ISO wholesale electricity market.