The push for a more sustainable electric supply has led various countries to adopt policies advocating the integration of renewable yet variable energy resources, such as wind and solar, into the grid. The challenges of integrating such time-varying, intermittent resources has in turn sparked a growing interest in the implementation of utility-scale energy storage resources (ESRs), with MWweek storage capability. Indeed, storage devices provide flexibility to facilitate the management of power system operations in the presence of uncertain, highly time-varying and intermittent renewable resources. The ability to exploit the potential synergies between renewable and ESRs hinges on developing appropriate models, methodologies, tools and policy initiatives. We report on the development of a comprehensive simulation methodology that provides the capability to quantify the impacts of integrated renewable and ESRs on the economics, reliability and emission variable effects of power systems operating in a market environment. We model the uncertainty in the demands, the available capacity of conventional generation resources and the time-varying, intermittent renewable resources, with their temporal and spatial correlations, as discrete-time random processes. We deploy models of the ESRs to emulate their scheduling and operations in the transmission-constrained hourly day-ahead markets. To this end, we formulate a scheduling optimization problem SOP whose solutions determine the operational schedule of the controllable ESRs in coordination with the demands and the conventional/renewable resources. As such, the SOP serves the dual purpose of emulating the clearing of the transmission-constrained day-ahead markets DAMs and scheduling the energy storage resource operations. We also represent the need for system operators to impose stricter ramping requirements on the conventional generating units so as to maintain the system capability to perform ``load following'', i.e., respond to quick variations in the loads and renewable resource outputs in a manner that maintains the power balance, by incorporating appropriate ramping requirement constraints in the formulation of the SOP. The simulation approach makes use of Monte Carlo simulation techniques to represent the impacts of the sources of uncertainty on the side-by-side power system and market operations. As such, we systematically sample the ``input'' random processes – namely the buyer demands, renewable resource outputs and conventional generation resource available capacities – to generate the realizations, or sample paths, that we use in the emulation of the transmission-constrained day-ahead markets via SOP. As a result, we obtain realizations of the market outcomes and storage resource operations that we can use to approximate their statistics. The approach not only has the capability to emulate the side-by-side power system and energy market operations with the explicit representation of the chronology of time-dependent phenomena – including storage cycles of charge/discharge – and constraints imposed by the transmission network in terms of deliverability of the energy, but also to provide the figures of merit for all metrics to assess the economics, reliability and the environmental impacts of the performance of those operations. Our efforts to address the implementational aspects of the methodology so as to ensure computational tractability for large-scale systems over longer periods include relaxing the SOP, the use of a ``warm-start'' technique as well as representative simulation periods, parallelization and variance reduction techniques. Our simulation approach is useful in power system planning, operations and investment analysis. There is a broad range of applications of the simulation methodology to resource planning studies, production costing issues, investment analysis, transmission utilization, reliability analysis, environmental assessments, policy formulation and to answer quantitatively various what-if questions. We demonstrate the capabilities of the simulation approach by carrying out various studies on modified IEEE 118- and WECC 240-bus systems. The results of our representative case studies effectively illustrate the synergies among wind and ESRs. Our investigations clearly indicate that energy storage and wind resources tend to complement each other in the reduction of wholesale purchase payments in the DAMs and the improvement of system reliability. In addition, we observe that CO2 emission impacts with energy storage depend on the resource mix characteristics. An important finding is that storage seems to attenuate the ``diminishing returns'' associated with increased penetration of wind generation. Our studies also evidence the limited ability of integrated ESRs to enhance the wind resource capability to replace conventional resources from purely a system reliability perspective. Some useful insights into the siting of ESRs are obtained and they indicate the potentially significant impacts of such decisions on the network congestion patterns and, consequently, on the LMPs. Simulation results further indicate that the explicit representation of ramping requirements on the conventional units at the DAM level causes the expected total wholesale purchase payments to increase, thereby mitigating the benefits of wind integration. The stricter ramping requirements are also shown to impact the revenues of generators that do not even provide any ramp capability services.
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Stochastic simulation of power systems with integrated renewable and utility-scale storage resources
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