Jingwei Yang

Dependent Discrete Convolution Based Probabilistic Load Flow for the Active Distribution System

Active distribution system (ADS) plays a significant role in enabling the integration of distributed generation. The stochastic nature of renewable energy resources injects the complex uncertainties of power flow into ADS. This paper proposes a discrete convolution methodology for probabilistic load flow (PLF) of ADS considering correlated uncertainties. First, the uncertainties of load and renewable energy are modeled using the distribution of the corresponding forecasting error, and the correlation is formulated using a Copula function. A novel reactive power-embedded DC power flow model with high accuracy in both branch flow and node voltage is introduced into ADS. Finally, the distribution of power flow is calculated using dependent discrete convolution, which is capable of handling nonanalytical probability distribution functions. In addition, a reduced dimension approximation method is proposed to further reduce the computational burden. The proposed PLF algorithm is tested on the IEEE 33-nodes system and 123-nodes system, and the results show that the proposed methodology requires less computation and produces higher accuracy compared with current methods.

A State-Independent Linear Power Flow Model With Accurate Estimation of Voltage Magnitude

Linearized power flow models are of great interest in power system studies such as contingency analyses and reliability assessments, especially for large-scale systems. One of the most popular models—the classical DC power flow model—is widely used and praised for its state independence, robustness, and computational efficiency. Despite its advantages, however, the DC power flow model fails to consider reactive power or bus voltage magnitude. This paper closes this gap by proposing a decoupled linearized power flow (DLPF) model with respect to voltage magnitude and phase angle. The model is state independent but is distinguished by its high accuracy in voltage magnitude. Moreover, this paper presents an in-depth analysis of the DLPF model with the purpose of accelerating its computation speed, leading to the fast DLPF (FDLPF) model. The approximation that is applied to obtain the FDLPF model from the DLPF model is justified by a theoretical derivation and numerical tests. The proposed methods are provably accurate and robust for several cases, including radial distribution systems, meshed large-scale transmission systems and ill-conditioned systems. Finally, expressions for sensitivity with regard to MW flow and bus voltage are provided as a potential application.

Effect of Natural Gas Flow Dynamics in Robust Generation Scheduling Under Wind Uncertainty

A new challenge has arisen in power generation scheduling recently, as the rapid increase in the number of gas-fired units has made power systems more vulnerable to failures in natural gas networks. The large-scale integration of wind power further exacerbates the problem because gas-fired units are usually scheduled to catch up wind power uncertainty and thus lead to great variations in the state of gas network. To meet this challenge, it is necessary to commit and dispatch the gas-fired units considering both wind uncertainty and natural gas network security. However, the dynamic characteristics of gas flow are remarkably slower than those of power flow, which should be appropriately modeled to explore its benefits for power system operation. Because directly applying partial differential equations overly complicates the already complicated generation scheduling problem, we address this problem by proposing an approximated transient matrix-form gas flow model. A two-stage robust generation scheduling model is then proposed considering the dynamic security constraints of gas networks and the wind power uncertainty. Moreover, we successfully avoid the nonlinearity of gas flow constraints by developing a new solution methodology. Finally, an illustrative case is presented to demonstrate the effect of gas network dynamics in generation scheduling.

Optimal Configuration Planning of Multi-Energy Systems Considering Distributed Renewable Energy

Multi-energy systems (MESs) contribute to increasing energy utilization efficiency and renewable energy accommodation by coupling multiple energy sectors. The preferable characteristic of MESs raises the need for optimizing the configuration of MESs across multiple energy sectors at the planning stage. Based on the energy hub (EH) model, this research presents a two-stage mixed-integer linear programming approach for district level MES planning considering distributed renewable energy integration. The approach models an MES as a directed acyclic graph with multiple layers. The proposed EH configuration planning procedure includes two stages: 1) optimizing what equipment (e.g., energy converters, distributed renewable energy sources and storages) should be invested in for each layer and 2) optimizing the connection relationships between the invested equipment in each two adjacent layers. The proposed approach is able to optimize both the equipment selection and the MES configuration. It can be applied to both expansion planning and initial planning of MESs from scratch. An illustrative example of planning a typical MES is given. A sensitivity analysis is performed to show the impacts of load profiles, energy prices and equipment parameters on the optimal MES configuration. A case study based on the MES in Beijing’s new subsidiary administrative center is conducted using the proposed approach.

Standardized Matrix Modeling of Multiple Energy Systems

Multiple energy systems (MESs) bring together the electric power, heat, natural gas, and other systems to improve the overall efficiency of the energy system. An energy hub (EH) models an MES as a device with multiple ports using a matrix coupling the inputs and outputs. This paper proposes a standardized matrix modeling method based on the concept of EH to build the coupling matrix automatically. The components and the structure of MES are first defined using graph theory. Then, the matrices describing the topology of the MES and the characteristics of the energy converters are developed. On this basis, the energy flow equations are formulated. Gaussian elimination can then be applied to obtain the coupling matrix and analyze the degree of freedom of the EH. A standard data structure for basic information on the MES is proposed to facilitate computerized modeling. Further, extension modeling of energy storage and demand response is also discussed. Finally, a case study of a modified tri-generation system is conducted to illustrate the proposed method.

Assessing the dispatch flexibility of coordinated solar and hydro generation

Dispatch flexibility denotes the capability of electrical generation that can be dispatched to serve the load. Hydro power generation with water reservoir has favorable dispatch flexibility that can be used to mitigate the intermittency and uncertainty of photovoltaic. Hydropower and photovoltaic can be coordinated by storing water energy in the peak hours of solar and shifting it to off-peak periods. In this paper, a metric of dispatch flexibility is defined to evaluate to what extent the solar and hydro generation can be coordinated. The methodology for measuring the dispatch flexibility of solar-hydro coordination is proposed based on a multi-scenario dispatch model considering the uncertainty of water inflow and solar irradiation. Results are drawn with a study on the first and largest solar-hydro coordination project in China, the Longyang Gorge (LYG) project. The study quantifies the generation dispatch flexibility of LYG project and illustrates the potential impact of different factors on solar-hydro coordinated generation.