Engineering topics

Du, Ershun

Publications and source records attributed to Du, Ershun.

8 records

DOE OSTI2021

Real-Time Dispatch With Secondary Frequency Regulation: A Pathway to Consider Intra-Interval Fluctuations

Real-time dispatch balances the power demand with minimized operating costs. For the current dispatch model, the demand is assumed to be constant within a time interval, while the intra-interval power balance is left to frequency regulation. Based on practical experience and simulations, this behavior may lead to insufficient frequency regulation and uneconomic regulation costs considering the increase in intra-interval fluctuations caused by renewables. Here, a real-time dispatch method with secondary frequency regulation behaviors is proposed. Without changing the interval of the real-time dispatch command, the system regulation mileage and intra-interval generation adjustment are explicitly formulated in a mixed-integer optimization model. To reduce the computational burden, an efficient two-stage calculation method is proposed. With practical utility data, this article finds that the proposed method can effectively improve the system frequency performance with the subminute net load forecasting curve obtained by an off-the-shelf forecasting approach. Time-domain simulations in IEEE and practical utility systems validate the effectiveness of the proposed method in terms of the frequency performance and total operating costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Characteristics of locational uncertainty marginal price for correlated uncertainties of variable renewable generation and demands

With the rapid increase of variable renewable energy sources in power systems, how to manage and price the uncertainty of renewable resources’ power outputs is becoming an urgent issue. Current market designs considering the uncertainties are mainly based on the probabilistic scenario set of demand and renewable energy resources power outputs. This consideration makes market designs vulnerable to three significant challenges when put into practice. First, the accurate probability distribution of renewable generation is hard to obtain in real-time. Second, it is challenging to clear the market timely with many scenarios to guarantee accuracy. Third, generation cost recovery cannot be guaranteed for some scenarios. To overcome these challenges, this paper proposes a locational uncertainty marginal price model to price the uncertainty explicitly based on a scenario-free stochastic market-clearing model. Instead of using the probabilistic scenario set, the uncertainty of renewable energy sources and loads is modeled with distributionally-robust chance constraints. The correlation of uncertainties can be endogenously modeled in both the market-clearing and the locational uncertainty marginal price formation. Furthermore, this paper proves that generation cost recovery, revenue adequacy, and partial market equilibrium can be achieved using the locational uncertainty marginal price model. Numerical results from both the small and large systems simulations validate that the generation cost recovery is maintained no matter the generation participates in uncertainty mitigation or not. The transmission congestion surplus is also allocated appropriately among loads, renewable energy sources, and financial transmission right owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Adjustable and distributionally robust chance-constrained economic dispatch considering wind power uncertainty

This paper proposes an adjustable and distributionally robust chance-constrained (ADRCC) optimal power flow (OPF) model for economic dispatch considering wind power forecasting uncertainty. The proposed ADRCC-OPF model is distributionally robust because the uncertainties of the wind power forecasting are represented only by their first- and second-order moments instead of a specific distribution assumption. The proposed model is adjustable because it is formulated as a second-order cone programming (SOCP) model with an adjustable coefficient. This coefficient can control the robustness of the chance constraints, which may be set up for the Gaussian distribution, symmetrically distributional robustness, or distributionally robust cases considering wind forecasting uncertainty. The conservativeness of the ADRCC-OPF model is analyzed and compared with the actual distribution data of wind forecasting error. The system operators can choose an appropriate adjustable coefficient to tradeoff between the economics and system security.

17 WIND ENERGY↗

DOE OSTI2019

Introducing Uncertainty Components in Locational Marginal Prices for Pricing Wind Power and Load Uncertainties

With substantially increasing penetration levels of wind power, electric power system flexibility is needed to address the variability and uncertainty of wind power output. Thus, it has become an urgent issue to obtain an optimal trade-off between economics and reliability, and to price system uncertainties. This paper proposes a new electricity market-clearing mechanism based on locational marginal prices (LMPs) for pricing uncertain generation and load. The uncertainty contained locational marginal price (U-LMP) is derived from a distributionally robust chance-constrained optimal power flow (DRCC-OPF) model in which only the first-order and second-order moments of the uncertain sources' probability distribution are needed. Compared with traditional LMPs, the proposed U-LMP formulation includes two new uncertainty components: transmission line overload uncertainty price and generation violation uncertainty price. These LMP uncertainty components are the price signals reflecting the system costs as a result of wind generation and demand uncertainty at different locations. Finally, using parametric case studies, the relationship among uncertainty levels, system generation cost, and LMP uncertainty components are established. Case studies performed on the PJM 5-bus and IEEE 118-bus systems verify the proposed U-LMP method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Modelling wind power spatial-temporal correlation in multi-interval optimal power flow: A sparse correlation matrix approach

The significantly increasing deployment of wind power necessitates that system operation considers the spatial-temporal correlation of power forecast from different wind power plants. How to model this spatial-temporal correlation in the actual system dispatch is challenging. In this paper, a sparse correlation matrix is utilized to efficiently model the spatial-temporal correlation of wind power forecast in the generation dispatch model. A novel, adjustable, and distributionally-robust chance-constrained multi-interval optimal power flow (ADRCC-MIOPF) model is proposed to obtain reliable economic dispatch (ED) solutions. The spatial-temporal correlation of wind power plants power forecasts is represented by the sparse correlation covariance matrix obtained from historical, time series wind power forecast error data. The chance constraints in the ADRCC-MIOPF model are transformed into a set of second-order-cone (SOC) constraints in which an adjustable coefficient in the chance constraints controls the robustness of the ADRCC-MIOPF model to the wind power forecast errors distribution. Case studies performed on the PJM 5-bus system and IEEE 118-bus system verify the proposed method to improve the system security and reduce the cost especially under the high wind penetration levels. All the cases can be solved within several minutes for both the small and large cases which validates the efficiency of the proposed sparse matrix model. In addition, considering the spatial-temporal correlation of wind power forecast and the distributional robustness of wind power forecast error leads to a more reliable economic dispatch with lower system violations.

17 WIND ENERGY↗

DOE OSTI2018

Value Streams from Distribution Grid Support Using Utility-Scale Vanadium Redox Flow Battery: NREL-Sumitomo Electric Battery Demonstration Project

The National Renewable Energy Laboratory (NREL) collaborated with Sumitomo Electric to provide research support in modeling and optimally dispatching a utility-scale vanadium redox flow battery (VRFB) energy storage. The objective of the project was to identify value streams from using utility-scale VRFB for local grid support use-cases, including voltage regulation (droop), capacity firming, peak shaving and valley filling, and energy arbitrage. This project was also supported by San Diego Gas and Electric (SDGandE). SDGandE provided data and approval to operate their VRFB energy storage system on an SDGandE distribution feeder. NREL worked with Sumitomo Electric to evaluate optimal dispatch strategies to VRFB, analyze the technical impacts, and calculate the associated cost-benefit ratio of substation-level energy storage on an SDG&E distribution feeder. To support this research, NREL evaluated the impacts of the battery use cases suggested by Sumitomo on the SDGandE host feeder. In addition to evaluating the battery use case, this effort identified possible ways to monetize the benefits from distribution feeder support.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Economic Justification of Concentrating Solar Power in High Renewable Energy Penetrated Power Systems

Concentrating solar power (CSP) plants are able to provide both renewable energy and operational flexibility at the same time due to its thermal energy storage (TES). It is ideal generation to power systems lacking in flexibility to accommodate variable renewable energy (VRE) generation such as wind power and photovoltaics. However, its investment cost currently is too high to justify its benefit in terms of providing renewable energy only. In this paper we evaluate the economic benefit of CSP in high renewable energy penetrated power systems from two aspects: generating renewable energy and providing operational flexibility to help accommodating VRE. In order to keep the same renewable energy penetration level during evaluation, we compare the economic costs between the system with a high share of VRE and another in which some part of the VRE generation is replaced by CSP generation. The generation cost of a power system is analyzed through chronological operation simulation over a whole year. The benefit of CSP is quantified into two parts: (1) energy benefit - the saving investment of substituted VRE generation and (2) flexibility benefit - the reduction in operating cost due to substituting VRE with CSP. The break-even investment cost of CSP is further discussed. The methodology is tested on a modified IEEE RTS-79 system. The economic justifications of CSP are demonstrated in two practical provincial power systems with high penetration of renewable energy in northwestern China, Qinghai and Gansu, where the former province has massive inflexible thermal power plants but later one has high share of flexible hydro power. The results suggest that the CSP is more beneficial in Gansu system than in Qinghai. The levelized benefit of CSP, including both energy benefit and flexibility benefit, is about 0.177-0.191 $/kWh in Qinghai and about 0.238-0.300 $/kWh in Gansu, when replacing 5-20% VRE generation with CSP generation.

14 SOLAR ENERGY↗

DOE OSTI2016

Managing Wind Power Uncertainty Through Strategic Reserve Purchasing

With the rapidly increasing penetration of wind power, wind producers are becoming increasingly responsible for the deviation of the wind power output from the forecast. Such uncertainty results in revenue losses to the wind power producers (WPPs) due to penalties in ex-post imbalance settlements. This paper explores the opportunities available for WPPs if they can purchase or schedule some reserves to offset part of their deviation rather than being fully penalized in the real time market. The revenue for WPPs under such mechanism is modeled. The optimal strategy for managing the uncertainty of wind power by purchasing reserves to maximize the WPP's revenue is analytically derived with rigorous optimality conditions. The amount of energy and reserves that should be bid in the market are explicitly quantified by the probabilistic forecast and the prices of the energy and reserves. A case study using the price data from ERCOT and wind power data from NREL is performed to verify the effectiveness of the derived optimal bidding strategy and the benefits of reserve purchasing. Additionally, the proposed bidding strategy can also reduce the risk of variations on WPP's revenue.

17 WIND ENERGY↗