Ramteen Sioshansi

Evaluating the Impacts of Real-Time Pricing on the Usage of Wind Generation

One of the impediments to large-scale use of wind generation within power systems is its nondispatchability and variable and uncertain real-time availability. Operating constraints on conventional generators such as minimum generation points, forbidden zones, and ramping limits as well as system constraints such as power flow limits and ancillary service requirements may force a system operator to curtail wind generation in order to ensure feasibility. Furthermore, the pattern of wind availability and electricity demand may not allow wind generation to be fully utilized in all hours. One solution to these issues, which could reduce these inflexibilities, is the use of real-time pricing (RTP) tariffs which can both smooth-out the diurnal load pattern in order to reduce the impact of binding unit operating and system constraints on wind utilization, and allow demand to increase in response to the availability of costless wind generation. We use and analyze a detailed unit commitment model of the Texas power system with different estimates of demand elasticities to demonstrate the potential increases in wind generation from implementing RTP.

The Value of Concentrating Solar Power and Thermal Energy Storage

This paper examines the value of concentrating solar power (CSP) and thermal energy storage (TES) in a number of regions in the southwestern United States. Our analysis shows that TES can increase the value of CSP by allowing more thermal energy from a CSP plants solar field to be used, allowing a CSP plant to accommodate a larger solar field, and by allowing CSP generation to be shifted to hours with higher energy prices. We analyze the sensitivity of this value to a number of factors, including the optimization period, price and solar forecasting, ancillary service sales, and dry cooling of the CSP plant, and also estimate the capacity value of a CSP plant with TES. We further discuss the value of CSP plants and TES net of capital costs.

Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation

One of the costs associated with integrating wind generation into a power system is the cost of redispatching the system in real-time due to day-ahead wind resource forecast errors. One possible way of reducing these redispatch costs is to introduce demand response in the form of real-time pricing (RTP), which could allow electricity demand to respond to actual real-time wind resource availability using price signals. A day-ahead unit commitment model with day-ahead wind forecasts and a real-time dispatch model with actual wind resource availability is used to estimate system operations in a high wind penetration scenario. System operations are compared to a perfect foresight benchmark, in which actual wind resource availability is known day-ahead. The results show that wind integration costs with fixed demands can be high, both due to real-time redispatch costs and lost load. It is demonstrated that introducing RTP can reduce redispatch costs and eliminate loss of load events. Finally, social surplus with wind generation and RTP is compared to a system with neither and the results demonstrate that introducing wind and RTP into a market can result in superadditive surplus gains.

OR Forum---Modeling the Impacts of Electricity Tariffs on Plug-In Hybrid Electric Vehicle Charging, Costs, and Emissions

Plug-in hybrid electric vehicles (PHEVs) have been touted as a transportation technology with lower fuel costs and emissions impacts than other vehicle types. Most analyses of PHEVs assume that the power system operator can either directly or indirectly control PHEV charging to coordinate it with power system operations. This paper examines the incentives of individual drivers making charging decisions with different electricity tariffs, and it compares the cost and emissions impacts of these charging patterns to the ideal case of charging controlled by the system operator. Our results show that real-time pricing performs worst among all of the tariffs we consider, because linear prices are inherently limited in signaling efficient use of resources in a system with nonconvexities. We also show that controlling overnight PHEV charging is significantly more important than limiting midday vehicle charging.

Economic Consequences of Alternative Solution Methods for Centralized Unit Commitment in Day-Ahead Electricity Markets

Many wholesale electricity markets call on the independent system operator (ISO) to determine day-ahead schedules for generators based on a centralized unit commitment. Up until recently, the Lagrangian relaxation (LR) algorithm was the only practical means of solving an ISO-scale unit commitment problem, and it was the solution technique used by most ISOs. Johnson et al. [1] demonstrate, however, that equity, incentive, and efficiency issues will arise from use of LR solutions, because different commitments that are similar in terms of total system costs can result in different surpluses to individual units. Recent advances in computing capabilities and optimization algorithms now make solution of the mixed-integer programming (MIP) formulation by means of branch and bound (B&B) tractable, often with optimality gaps smaller than those of LR algorithms, which has led some ISOs to adopt B&B algorithms and others proposing to do so. With the move towards B&B, one obvious question is whether the use of MIP will eliminate or reduce the issues with LR raised by Johnson et al. Using actual market data from an ISO, we demonstrate that both LR and MIP solutions will suffer the same equity issues, unless the ISO unit commitment problems can be solved to complete optimality within the allotted timeframe-which is beyond current computational capabilities. Our results further demonstrate that the size of the payoff deviations are not monotone in the size of the optimality gap, meaning smaller optimality gaps from B&B will not necessarily mitigate the issues Johnson et al. raise. We show that the use of make-whole payments, which ensure units recover any startup and no-load costs not recovered by inframarginal energy rents, can help to reduce surplus volatility and differences to some extent.

How Thermal Energy Storage Enhances the Economic Viability of Concentrating Solar Power

This paper examines the economic performance and rationale of concentrating solar power (CSP) with and without thermal energy storage (TES). We demonstrate that TES can increase the energy and capacity value of CSP and also show that adding TES to a CSP plant can increase its economic viability by increasing its operating revenues to the point that the capital cost of CSP can be justified.

Market and Policy Barriers to Deployment of Energy Storage

There has recently been resurgent interest in energy storage, due to a number of developments in the electricity industry. Despite this interest, very little storage, beyond some small demonstration projects, has been deployed recently. While technical issues, such as cost, device efficiency, and other technical characteristics are often listed as barriers to storage, there are a number of non-technical and policy-related issues. This paper surveys some of these main barriers and proposes some potential research and policy steps that can help address them. While the discussion is focused on the United States, a number of the findings and observations may be more broadly applicable.

A Dynamic Programming Approach to Estimate the Capacity Value of Energy Storage

We present a method to estimate the capacity value of storage. Our method uses a dynamic program to model the effect of power system outages on the operation and state of charge of storage in subsequent periods. We combine the optimized dispatch from the dynamic program with estimated system loss of load probabilities to compute a probability distribution for the state of charge of storage in each period. This probability distribution can be used as a forced outage rate for storage in standard reliability-based capacity value estimation methods. Our proposed method has the advantage over existing approximations that it explicitly captures the effect of system shortage events on the state of charge of storage in subsequent periods. We also use a numerical case study, based on five utility systems in the U.S., to demonstrate our technique and compare it to existing approximation methods.

Using Price-Based Signals to Control Plug-in Electric Vehicle Fleet Charging

We study decentralized plug-in electric vehicle (PEV) charging control, wherein the system operator (SO) sends price-based signals to a load aggregator (LA) that optimizes charging of a PEV fleet. We study a pricing scheme that conveys price and quantity information to the LA and compare it to a simpler price-only scheme. We prove that the price/quantity-based mechanism can yield a socially optimal solution. We also examine several numerical case studies to demonstrate the superior performance of the price/quantity-based scheme. The price/quantity scheme yields nearly identical PEV charging costs compared to the social optima, whereas the price-only scheme is highly sensitive to the choice of a regularization penalty term that is needed to ensure convergence. We also show that the time to compute an equilibrium with the price-only mechanism can be up to two orders of magnitude greater than with the price/quantity scheme and can involve 24 times more information exchange between the SO and LA.

Estimating the Capacity Value of Concentrating Solar Power Plants With Thermal Energy Storage: A Case Study of the Southwestern United States

We estimate the capacity value of concentrating solar power (CSP) plants with thermal energy storage (TES) in the southwestern U.S. Our results show that incorporating TES in CSP plants significantly increases their capacity value. While CSP plants without TES have capacity values ranging between 60% and 86% of maximum capacity, plants with TES can have capacity values between 79% and 92%. We demonstrate the effect of location and configuration on the operation and capacity value of CSP plants. We also show that using a capacity payment mechanism can increase the capacity value of CSP, since the capacity value of CSP is highly sensitive to operational decisions and energy prices are not a perfect indicator of scarcity of supply.