Abstract
Due to the absence of historical data and the errors of measurement instruments, there may be uncertainties in the distribution parameters of the random variables describing the uncertain fluctuations of node power including renewable energy station output and load power in the combined cooling heating and power (CCHP) campus microgrid. In this paper, intervals are used to describe the uncertainties of distribution parameters of the random variables, and an interval probabilistic energy flow (IPEF) calculation model of the CCHP campus microgrid is established. Introducing the interval arithmetic (IA) into the cumulant method, an IA-based IPEF algorithm is proposed to obtain the analytical expressions of probability density function or cumulative distribution function intervals of the state variables. Moreover, affine arithmetic (AA) is introduced to address the interval extension problem in the calculation, and an AA&IA-based IPEF algorithm is proposed. By constructing the correlation transformation matrixes, the correlation among different node power is considered in the IPEF calculation. A case study on a CCHP campus microgrid demonstrates that the results of the AA&IA-based IPEF algorithm are more accurate than those of the IA-based IPEF algorithm by using the results of the double-layer Monte Carlo method as a reference. Moreover, the proposed algorithms are more efficient than the double-layer Monte Carlo method.
Highlights
combined cooling heating and power (CCHP) technology can fully recycle the wasted heat of gasfired power generators for cooling and heating, which can effectively increase the utilization rate and realize the cascade utilization of energy
This paper makes three contributions: 1) To reflect the uncertain fluctuations of node power including renewable energy station output and load power more accurate, intervals are used to describe the uncertainties of distribution parameters of node power random variables, and an interval probabilistic energy flow (IPEF) calculation model of a CCHP-CMG is established considering the interval uncertainties of distribution parameters
When the correlation coefficients of load power and solar irradiance both reach 0.9, the errors of voltage amplitude and angle and pipeline flow rate are less than 0.25%, and the temperature error is less than 0.5%
Summary
Mark of heating/cooling network, 1/−1 respectively represent heating/cooling network. Total wasted heat power and electrical power of CCHP unit Input/output power of energy conversion components Injected power vector at buses or nodes State variable vector Augmented vector of injected power Reference value of W/X/F Random fluctuation of W/X/F Sensitivity matrix k-order cumulants vectors of X and W k-order CI vector of X and W k-order CI vector of load power k-order CI of load power fluctuation. KL, KS k-order origin moment interval of r k-order CI of r k-order CI of active/reactive power of PV station k-order CI of injected power of PV/PT station k-order CI of PV/PT power fluctuation Random fluctuations of node power augmented vectors of loads and renewable energy stations considering the correlation. Decorrelated random fluctuations of node power augmented vectors of loads and renewable energy stations Correlation conversion matrixes corresponding to the node power of loads and renewable energy stations. SUBSCRIPTS AND SUPERSCRIPTS (·) Calculate the kth power of each element in the matrix (·)e/h/c Variables of electricity/heating/cooling network
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