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A Credible Capacity Assessment Method for New Energy Sources Based on Time Series Stochastic Production Simulation
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Capacity credit (CC) can be defined as the capacity of conventional generators that can be replaced by renewable energy sources (RES) and/or other resources such as energy storage without reducing system reliability. Conventional approaches for calculating CC typically treat the power system as a single area without considering transfer constraints and reliability of interconnectors. However, in multi-area power systems locational aspects are key to assess tradeoffs and synergies arising from transmission, storage and RES in providing adequacy of supply. In this work, we propose a new methodology to quantify the CC of RES and storage in a multi-area power system. Due to the large computational burden brought about by composite system adequacy evaluation, a new accelerated sequential Monte Carlo simulation strategy and a new adaptive sampling approach are specifically developed to achieve computation efficiency. The proposed methodology is tested by demonstrating the impact of interconnectors' transfer constraints and availability on RES-storage CC in the case of the Australian National Energy Market (NEM) multi-area power system, with applications to wind, solar, and pumped hydro storage plants. The results from several realistic NEM case studies highlight how the proposed model can inform strategic, reliability-aware integrated system planning of large-scale interconnected low-carbon power systems.
Environmental concerns and depletion of traditional energy lead to the booming development of renewable distributed generation (RDG) in the past decade. However, due to intermittent nature of renewable energy sources, to what extent RDG could provide capacity to power systems becomes a critical issue to the utility company when implementing long-term system strategic (generation expansion) planning. On the other hand, in a smart-grid frame, the popularization of different varieties of demand-side resources enables the system to operate at more flexible modes ever before. The potential variability in load demand not only introduces additional dynamics and uncertainties to the system, but could also affect the reliability benefits of RDG. Therefore, in practice, to effectively estimate the reliability value of RDG in power systems, the potential interaction between generation- and demand- sides must be properly captured. In this paper, a study to assess the capacity credit (CC) of RDG in a context of distributed generation system (DGS) is performed with consideration of the impacts of demand response (DR). A compound reliability model for DR is presented which considers the uncertainties involved in both instant response and follow-on load recovery processes. On this basis, an assessment framework for the CC of RDG based on sequential Monte Carlo Simulation (SMCS) is developed by which the inter-temporal characteristics of DR resources can be fully captured. The numerical study is implemented based on the IEEE-38 bus test case. The calculation results demonstrate that the CC of RDG would depend on a variety of factors, including penetration level, responsiveness of load demand and the correlations between RDG and DR availability. Also, it is shown that accounting for the underlying effect of DR is of absolute importance, otherwise the CC of RDG might be estimated erroneously in real practices.
The goals of achieving carbon peak and carbon neutrality will drive the energy structure transition, and the large-scale integration of distributed energy resource (DER) will become a prominent problem. In order to ensure the safe and stable operation of large power grids and support the sustainable accommodation of renewable energy, the form of DER participating in the security and stability control of large power grids is discussed, and an interaction mechanism based on virtual power plants (VPPs) is proposed. Firstly, the evolution of power systems driven by the dual carbon goals is analyzed, and the challenges faced by its safe and stable operation are analyzed. Secondly, the definition, composition structure and functional characteristics of the VPP are elaborated, and the connotation of integrating massive heterogeneous DERs to interact friendly with large power grids is revealed. Then, the hierarchical dispatch and control architecture of the VPP is built based on the multi-agent technology. And the mechanism of the inner operation and the participation in the security and stability control of large power grids of the VPP is proposed. Finally, it is pointed out that VPP is a new technology form that accommodates a high proportion of renewable energy in the new power system, and there are several technical issues in this field that need to be focused on.
Processing data properly and constructing a rational photovoltaic output model is the base of power system operation and plan. Focusing on characteristics of photovoltaic output, a photovoltaic output clustering and simulation method based on Markov model and spectral clustering is proposed. Firstly, the amplitude parameter, the standard component and fluctuant component are extracted from photovoltaic output and Markov model is adopted to analyze the fluctuant component. We use spectral clustering and Alpha value, Silhouette index and Bayesian information criterion to evaluate the quality of clustering results and to determine the optimal cluster number. Then, state-transition matrixes between the weather in the month and power output in the day are integrated, and a bistratal Markov model is constructed. Finally, the mid/long term photovoltaic simulating output is generated by utilizing the bistratal sampling. Compared with the K- means clustering analysis, spectral clustering is more suitable for processing photovoltaic output data, especially in the condition of data missing or data error and in distinguishing weather data. Through the comparative analysis on statistical characters, timing characters and weather characters, the validity of the proposed model is verified. © 2019 State Power Economic Research Institute. All rights reserved.
Owing to increasing photovoltaic (PV) systems installation, especially large-scale ground-mounted PV plants, the role of PV power generation is gradually changing from a supplementary energy to an alternative energy resource, with respect to conventional power generation. It is important and necessary to evaluate the capacity value of PV systems. This paper introduces an artificial neural network-based empirical model to evaluate a PV plant's capacity credit, in which the effective load carrying capability is utilized to calculate the capacity value of utility-scale PV plants. A novel metric is proposed to depict the temporal correlation between PV output and load profile, considering the intermittent nature of PV power. The impacts of PV penetration, simulation temporal granularities, and correlation similarity of varying PV and load time series on the capacity credit evaluation of PV systems are also explored. Finally, the presented empirical model is employed for capacity credit evaluation for any given conditions. The law of decreasing marginal capacity value is verified, and the optimal time scale for capacity credit estimation is obtained using the established model.
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