The worldwide promotion of electric heating improves the operational flexibility of the power system (PS). However, it also increases the risk of cascading failure due to the bi-directional interaction with the heating system (HS). To prevent such contingencies and facilitate prompt post-outage adjustments, the steady-state security region (SSR) of the integrated energy system (IES) will be a valuable and effective tool. This article derives an accurate SSR model for the heat and electricity IES, composed of a novel combined heat and power (CHP) unit model, the AC power flow model, and the dynamic HS model. An equivalence-based hyperplane method (EHM) is then proposed to solve the SSR model. The EHM treats the SSR as the intersection of the time-invariant and time-varying parts, and each part is described with a set of hyperplanes. On this basis, the sequential SSR (SSSR) is defined for IES to characterize the impact of CHP and HS integration, with which the time-varying process resulting from the thermal dynamics is divided into three periods for investigation. Numerical studies verify the accuracy and efficiency of the SSSR and EHM and demonstrate the influences of the CHP, HS integration, and thermal dynamics on the secure operation of the PS.

Energy flow analysis is a fundamental tool to determine the network states of the integrated energy systems (IES). For the widely deployed IES with coupled power grids (PG) and heating networks (HN), we still lack a generic tool that can be easily employed to calculate the quasi-dynamic energy flows of it. In this paper, we have developed a generic code package (named as MATHN) for energy flow analysis of the quality-regulated HN. After that, a generic solution framework that leverages MATPOWER and MATHN to realize decomposed energy flow calculation of PG and HN is proposed. Behind the MATHN tool, a model reformulation technique is proposed to eliminate the massive indirect variables of the basic HN model discretized by finite difference, which finally derives a compact matrix formulation (similar to the network equation of power grid: $\boldsymbol {I}=\boldsymbol {Y}\boldsymbol {U}$ ) for generic description of any HN and also slashes the original model scale by around 20 times. Moreover, the solution strategy behind the MATHN tool further converts this matrix formulation into a standard system of non-homogeneous linear equations, with which some existing well-recognized algorithms can be directly applied for efficient and accurate solution. Case studies on three different scales of test systems demonstrate the generality, efficiency and accuracy of our methods. All the codes and input data are packaged into the MATHN tool and are made open-source for non-commercial use.

High penetration of distributed photovoltaics (PVs) has caused severe voltage violation problem in distribution networks. In view of the actual situations, this paper proposes a coordinated optimization method for distributed PVs clusters considering inadequate measurement data. Based on pivotal measurement data, each cluster firstly uses network simplification method to obtain a simplified network with complete measurement information. And then each cluster implements coordinated optimization with adjacent clusters to minimize its network losses, PVs generation losses and voltage violation amount of downstream cluster. The coordinated optimization includes two stages, i.e. evaluating voltage regulation capability of intra-cluster reactive power resources and implementing cluster autonomous optimization control. These two stages can use the reactive power resources of upstream clusters to solve the voltage violation of downstream clusters, which avoids unnecessary generation losses of distributed PVs. The effectiveness and accuracy of the proposed method are demonstrated via simulation tests on a real 10kV feeder in China and modified IEEE 123-bus system. Compared with distributed optimization method, the proposed coordinated optimization requires much less calculation time and communication data between clusters.

For a tourist area (TA), energy utilization is mostly concentrated in certain period of time. Therefore, the peak load is several times more than the average load. A Micro Energy Network Integrated with Renewables (MENR) system is considered as a potential solution to mitigate this problem. To design an appropriate MENR system, a multi-objective energy quality management (EQM) method based on the Genetic Algorithm is proposed. Here, EQM aims at reducing the primary energy consumption and optimizing the energy shares of various renewables in a MENR system. In addition to minimizing life-cycle costs and maximizing the exergy efficiency of a MENR system, the issue of system reliability is addressed. Then, a case study is presented, where the EQM method is applied to a TA located in Dali, China. Three possible reference MENR scenarios are analyzed. After confirming the reference scenarios, advanced MENR scenarios with improved system reliability are discussed. The rest of the work is dedicated to investigating the effects of various energy storage systems (ESSs) parameters and the number of electric vehicles (EVs) on MENR scenarios. The results suggest that there are significant differences between various MENR scenarios depending on the number of EVs and the investment reduction of ESSs.