Tim Mount

The Big Picture: Smart Research for Large-Scale Integrated Smart Grid Solutions

This article represents an edited version of opinions expressed in an extensive white paper created by many individuals associated with the Power Systems Engineering Research Center (PSERC) of the National Science Foundation (NSF) and posted on PSERCs Web site, www.pserc. org. The four tasks described above are considered crucial to smart grid R&D, demonstration, and eventual deployment. As learning and innovation occur during the course of a demonstration, changes may be needed in the architecture, the components, and the way they are integrated operationally. The goal is to acquire the best information possible for the eventual decisions on whether and how an integrated smart grid solution should be implemented, so adjusting demonstrations as needed to provide that information is very appropriate. It is also important that demonstrations be designed and implemented to gain the knowledge needed for a system wide deployment of a smart grid. The bulk transmission system should be included in the design. There are a great number of unknowns in moving toward the national goal of a low-carbon economy. That uncertainty can be reduced by effectively designed large-scale demonstrations drawing on the results of prior R&D efforts.

Innovative developments in load as a reliability resource

This paper reports on work the Consortium for Electric Reliability Technology Solutions (CERTS) has been pursuing to hasten the arrival of meaningful load participation in competitive electricity markets. The activities include: experimental economic analysis of the effect of price responsive load in reducing market prices and price volatility; assessments of emerging demand response programs and technologies for enabling customer participation in electricity markets, and demonstrations of load in providing ancillary services (notably, spinning reserve).

A revenue sensitivity approach for the identification and quantification of market power in electric energy markets

In this paper we present a practical approach for identifying and measuring market power in an electric energy market. To do so we determine which participants or groups of participants have the ability to increase their own revenues without affecting the rest of the market, and then apply a relative measure to quantify the extent of market power exploitation. We present a 30-bus, 6-generator example in which two generators in a load pocket are found to have and use market power. Using price and revenue signals from a repeated auction, we explain how these generators learn to exploit their power over time. Experimental results are also presented and analyzed.

Testing the effects of price responsive demand on uniform price and soft-cap electricity auctions

Testing auction mechanisms experimentally in a controlled environment provides an inexpensive means for evaluating their relative merits. This paper describes a framework for testing the efficacy of a price-responsive load on a uniform-price last-accepted offer and a soft-cap market. Experimental evidence to date, based on uniform-price market testing has shown an ability of price-responsive load to mitigate high volatility and average price. The paper addresses a process to validate these results as well as our hypothesis that price-responsive load mitigates high soft-cap market price behavior, such as that observed in California.

Scheduling of Energy Storage Systems with Geographically Distributed Renewables

Renewable energy sources (RES) are very likely to continue the upward capacity trend witnessed in the past years. The reasons for adoption are varied and respond to both market pressures, influence of government intervention and a raised awareness of the consequences on the environment of the current generation fleet. The change from dispatch able generation to an environment in which Independent System Operators (ISOs), Regional Transmission Operators (RTOs) and consumers, among others, accommodate the demand to the available generation, requires fundamental changes in the way the system is managed. Also, to better harness the energy from renewable sources, both new methods and technologies need to be adopted, counteracting for the sometimes unpredictable behavior of these sources. This study proposes a method with multi-period optimization to help prescribe the optimal placement and usage of RES and Energy Storage Systems (ESS) with full information of the system. Four cases are analyzed in their dispatches, as well as the benefits to the participants in the wholesale market, for a reduced 30-bus network. While the data requirements are high, and the use of a reduced system limits the applications herein proposed, the policy implications from the results obtained provide useful insights into an ongoing debate regarding on how to direct investment in the electrical system.

Are existing ancillary service markets adequate with high penetrations of variable generation

The inherent variability of wind generation may 1) increase the operating costs of the conventional generators used to follow the net load not supplied by wind capacity (i.e. due to additional ramping costs), and 2) increase the amount of reserve conventional generating capacity needed to maintain Operating Reliability. For customers, the higher operating costs for conventional generators caused by additional ramping are largely offset by lower wholesale prices, due to reductions in the total annual generation from fossil fuels. However, the lower wholesale prices (

Alternate mechanisms for integrating renewable sources of energy into electricity markets

/MWh) imply lower annual earnings for conventional generators that lead to higher amounts of missing money (

Dynamic optimization for the management of stochastic generation and storage

/MW) needed to maintain the Financial Adequacy of installed generating units. In addition, the operating costs for the generating units that provide ramping will be higher, and these costs are not covered in the standard markets that supply regulation. The objective of this paper is to determine how wind variability affects the optimal hour-to-hour dispatch of generating units and the corresponding operating costs and wholesale prices. The results show that incorporating the cost of ramping can have substantial effects on these costs and on the optimum amount of wind capacity dispatched and the amount of reserve generating capacity needed for reliability. The Cornell SuperOPF is used to illustrate how the operating costs and wholesale prices can be determined for a reliable network (the amount of conventional generating capacity needed to maintain Operating Reliability is determined endogenously). In previous research using the SuperOPF, the analyses have been based on optimizing the dispatch and reserves for typical hours. In contrast, the results in this paper use a typical daily pattern of load and capture the cost of ramping by including additions to the operating costs of the generating units associated with the hour-to-hour changes in their optimal dispatch. The calculations for determining endogenous up and down reserves are included, and the wind generation cost is assumed to be zero. Additionally, the maximum and minimum available capacities for all hours in the day are constrained to the optimal capacities for the hours with the highest and the lowest loads. Different scenarios are evaluated for a given hourly realization of wind speeds using specified amounts of installed wind capacity with and without ramping costs. The analysis also evaluates the effects of eliminating network constraints and the daily variability of the wind resource.

The economic value of transmission lines with increased penetrations of stochastic generation

The objective of this paper is to contrast the effect of demand side versus supply side policies aimed at operating a secured system, while maintaining the sustainability of the system by analyzing: 1) the role that load following costs can have in counteracting the impact of unpredictable Renewable Energy Sources (RES) on system operation and 2) The optimal management of Deferrable (or controllable) demand, given the inter-temporal constraints they face, to be coupled with RES. This will extend the concept of controllable loads to include thermal storage, and in particular, the use of ice batteries to replace standard forms of air-conditioning (AC). The analysis is done by simulation in Matpower ([1]) for a Multi-period, stochastic, security constrained AC optimal power flow. This is a continuation of work in stochastic AC-OPF modeling ([2]). A set of constraints reflecting specific ramping costs for all generation is included. The expected amount of Load Not Served (LNS) is also endogenously solved. Wind is modeled as the RES, with a characterization similar to historical data from New York and New England. The network model is a reduction of the Northeastern Power Coordinating Council (NPCC, [3]), modified to focus on New York and New England. Since the adoption of renewables leads to higher cost of capacity for conventional generation, new investments need to be made to be able to manage the load in more economical ways. A load-following ramping reserve product is proposed as an example of a mechanism for participants to signal their technical characteristics and constraints. Investments in storage and controllable load management can also improve the system efficiency. Our results illustrate the importance of market designs that provide participants with the correct economic incentives and signaling mechanisms.

Experimental results for single period auctions

In order to increase the amounts of renewable energy accommodated in the system, new tools that take into account the horizon of the decision taken are necessary. Feature like the availability of new information can be included in a dynamic optimization framework and therefore help mitigate congestion in the system and have positive effects on distribution systems. This study proposes a new algorithm and shows some preliminary results for the use of Energy Storage Systems (ESS) interacting with stochastic sources of generation. The initial motivation came from the study of the adoption of renewables for electricity, and how to better harness the power of sources that are inherently oscillatory in power output. The benefits of ESS in a dynamic optimization go beyond the amount of renewable energy actually dispatched in the system. The current debate and probable adoption of electrified transportation will most likely increase the pressure on local distribution systems. However, the availability of distributed energy will also increase, in the form of energy storage, once the interface between the grid and the power sources in the vehicles is developed in a mass scale.