Seyed Hossein Madaeni

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.

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.

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.

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

We estimate the capacity value of concentrating solar power (CSP) plants without thermal energy storage in the southwestern U.S. Our results show that CSP plants have capacity values that are between 45% and 95% of maximum capacity, depending on their location and configuration. We also examine the sensitivity of the capacity value of CSP to a number of factors and show that capacity factor-based methods can provide reasonable approximations of reliability-based estimates.

Comparing Capacity Value Estimation Techniques for Photovoltaic Solar Power

In this paper, we estimate the capacity value of photovoltaic (PV) solar plants in the western U.S. Our results show that PV plants have capacity values that range between 52% and 93%, depending on location and sun-tracking capability. We further compare more robust but data- and computationally-intense reliability-based estimation techniques with simpler approximation methods. We show that if implemented properly, these techniques provide accurate approximations of reliability-based methods. Overall, methods that are based on the weighted capacity factor of the plant provide the most accurate estimate. We also examine the sensitivity of PV capacity value to the inclusion of sun-tracking systems.

Measuring the Benefits of Delayed Price-Responsive Demand in Reducing Wind-Uncertainty Costs

Demand response has benefits in mitigating unit commitment and dispatch costs imposed on power systems by wind uncertainty and variability. We examine the effect of delays in consumers responding to price signals on the benefits of demand response in mitigating wind-uncertainty costs. Using a case study based on the ERCOT power system, we compare the cost of operating the system with forecasts of future wind availability to a best-case scenario with perfect foresight of wind. We demonstrate that wind uncertainty can impose substantive costs on the system and that demand response can eliminate more than 75% of these costs if loads respond to system conditions immediately. Otherwise, we find that with a 30-min lag in the response, nearly 72% of the value of demand response is lost.

Using Demand Response to Improve the Emission Benefits of Wind

Although wind generation is emissions- and cost-free, real-time output can be highly variable and uncertain. This can require additional conventional generating capacity to be committed. Since the efficiency of emissions controls can depend on generator loading, this additional capacity can increase generator emissions rates. Another method of accommodating wind is using demand response, which has system loads that more closely follow supply. Using a case study based on the Texas power system, we examine the emissions and cost impacts of using these two strategies to accommodate wind. While we find that wind decreases loading and increases emissions rates of generators, it has a positive net emissions benefit overall. We also find that while demand response reduces some of the emissions benefits of wind, combining wind and demand response provides more cost-effective emissions abatement than wind alone.

Comparison of Capacity Value Methods for Photovoltaics in the Western United States

This report compares different capacity value estimation techniques applied to solar photovoltaics (PV). It compares more robust data and computationally intense reliability-based capacity valuation techniques to simpler approximation techniques at 14 different locations in the western United States. The capacity values at these locations are computed while holding the underlying power system characteristics fixed. This allows the effect of differences in solar availability patterns on the capacity value of PV to be directly ascertained, without differences in the power system confounding the results. Finally, it examines the effects of different PV configurations, including varying the orientation of a fixed-axis system and installing single- and double-axis tracking systems, on the capacity value. The capacity value estimations are done over an eight-year running from 1998 to 2005, and both long-term average capacity values and interannual capacity value differences (due to interannual differences in solar resource availability) are estimated. Overall, under the assumptions used in the analysis, we find that some approximation techniques can yield similar results to reliability-based methods such as effective load carrying capability.

The capacity value of solar generation in the Western United States

We compare reliability- and capacity factor-based methods of estimating the capacity value of solar power plants. Our results show that solar plants can have long-term capacity values ranging between 46% and 95% of maximum capacity, depending on the solar technology, plant configuration, and location. We also show that the capacity-factor based methods provide reasonable approximations solar capacity values.