A Decision Framework for Optimal Pairing of Wind and Demand Response Resources

Day-ahead electricity markets do not readily accommodate power from intermittent resources such as wind because of the scheduling difficulties presented by the uncertainty and variability in these resources. Numerous entities have developed methods to improve wind forecasting and thereby reduce the uncertainty in a day-ahead schedule for wind power generation. This paper introduces a decision framework for addressing the inevitable remaining variability resulting from imperfect forecasts. The framework uses a paired resource, such as demand response, gas turbine, or storage, to mitigate the generation scheduling errors due to wind forecast error. The methodology determines the cost-effective percentage, or adjustment factor, of the forecast error to mitigate at each successive market stage, e.g., 1 h and 10 min ahead of dispatch. This framework is applicable to any wind farm in a region with available pairing resources, although the magnitude of adjustment factors will be specific to each region as the factors are related to the statistics of the wind resource and the forecast accuracy at each time period. Historical wind data from New England are used to illustrate and analyze this approach. Results indicate that such resource pairing via the proposed decision framework will significantly reduce the need for an independent system operator to procure additional balancing resources when wind power participates in the markets

Wind Power Uncertainty and Power System Performance

The penetration of wind power into global electric power systems is steadily increasing, with the possibility of 30% to 80% of electrical energy coming from wind within the coming decades. At penetrations below 10% of electricity from wind, the impact of this variable resource on power system operations is manageable with historical operating strategies. As this penetration increases, new methods for operating the power system and electricity markets need to be developed. As part of this process, the expected impact of increased wind penetration needs to be better understood and quantified. This paper presents a comprehensive modeling framework, combining optimal power flow with Monte Carlo simulations used to quantify the impact of high levels of wind power generation in the power system. The impact on power system performance is analyzed in terms of generator dispatch patterns, electricity price and its standard deviation, CO2 emissions and amount of wind power spilled. Simulations with 10%, 20% and 30% wind penetration are analyzed for the IEEE 39 bus test system, with input data representing the New England region. Results show that wind power predominantly displaces natural gas fired generation across all scenarios. The inclusion of increasing amounts of wind can result in price spike events, as the system is required to dispatch down expensive demand in order to maintain the energy balance. These events are shown to be mitigated by the inclusion of demand response resources. Benefits include significant reductions in CO2 emissions, up to 75% reductions at 30% wind penetration, as compared to emissions with no wind integration

Stochastic Synthetic Data Generation for Electric Net Load and Its Application

The increasing integration of renewable energy in electric power systems focuses attention on realistic representation of ”net load” because it aggregates the information from both demand and the renewable supply side; net load is the remaining demand that must be met by non-renewable resources. However, the net load data is not readily accessible because of cost, privacy, and security concerns. Furthermore, even if historical data is available, multiple stochastic scenarios are often needed for a wide range of power system applications. To address these issues, this paper proposes a stochastic synthetic net load profile generation approach. A seasonal detrending technique is combined with the modified Fractional Gaussian Noise method to deal with the complex multi-periodic seasonal trends in the net load profile. A thorough statistical validation and temporal correlation check are performed to show the quality of the synthetic data. The benefits of the synthetic data are demonstrated by a microgrid energy management problem

Estimating the System Costs of Wind Power Forecast Uncertainty

Uncertainty in forecasts of wind power generation raises concerns of integrating wind power into power system operations and electricity markets at acceptable costs. The analysis presented in this paper uses an optimal power flow (OPF) model in a Monte Carlo Simulation (MCS) framework to estimate the additional cost of power system operation with uncertain output from a wind farm. A base case dispatch is established along with alternate dispatches based upon a probability distribution of real time wind power generation. The cost of the uncertainty in wind power forecasts is then quantified in terms of the difference in production cost between the base case and the cost for system dispatch under scenarios drawn from the distribution of real time wind power generation. Using various regional load levels and ramp capabilities of other generators, the results from the OPF and MCS show that wind power forecast uncertainty for the test system can increase production cost between 2.5% and 11%

A Spatiotemporal Analysis of New York State Grid Transition under the CLCPA Energy Strategy

To decarbonize the energy sector, clean energy plans with a tremendous quantity of renewable energy integration are emerging globally. New York State (NYS) has one of the most ambitious targets to realize carbon-neutrality by 2040. To investigate the feasibility of this plan, the starting point of the plan is analyzed on a modified representation of the NYS power grid. Historical data for 2019 is used to model the spatiotemporal co-variability of load and virtual renewable outputs at hourly intervals. Optimal power flow analysis is simulated on daily basis for the full year to examine the performance of the system from annual to hourly levels. Results identify bottlenecks to using renewable energy efficiently and reliably with an emphasis on storage units, providing system operators, policymakers, and stakeholders with a practical research foundation