To capture the stochastic characteristics of renewable energy generation output, the chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical solution for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenar-io-based methods are used to approximate probability with heavy computation burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quan-tiles and transform chance constraints into deterministic con-straints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.

The tight coupling of power distribution system (PDS) and district heating system (DHS) makes it imperative to devise a coordinated strategy for the resilience enhancement of integrated electric and heating system (IEHS) in natural disasters. The PDS reconfiguration serves as a critical tool for service restoration by the optimal adjustment of PDS topology with designated tie and sectionalizing switches. Similarly, the DHS reconfiguration provides a viable solution for countering disasters by the optimal adjustment of heating supply topology with targeted tie and sectionalizing valves in DHS. In this paper, a multi-stage resilience enhancement strategy considering the coordinated reconfiguration of PDS and DHS is proposed, which coordinates degradation, fault isolation, and service restoration stages after disasters. The proposed model is formulated as a mixed-integer nonlinear problem, which addresses the coordinated regulation of PDS switches and DHS valves to achieve efficient fault isolation and provide a better service restoration. To make the proposed model tractable, a linearized Distflow model is proposed by introducing current-oriented auxiliary variables and the approximation of power flow and voltage drop equations. Extensive case studies are conducted to validate the efficiency and accuracy of the proposed model and illustrate the performance of coordinated reconfiguration of PDS and DHS on resilience enhancement.

To stabilize the frequency of the renewable energy sources (RESs) dominated power system, frequency supports are required by RESs through virtual inertia emulation or droop control in the newly published grid codes. Since the long-term RES prediction involves significant errors, we need online configure the frequency control parameters of RESs in a rolling manner to improve the operation economics under the premise of stabilizing system frequency. To address this concern, this paper proposes a frequency constrained stochastic look-ahead power dispatch (FCS-LAPD) model to formulate the frequency control parameters of RESs and energy storage systems (ESSs) as scheduling variables, which can optimally allocate the virtual inertia and droop coefficient of RESs and ESSs. In this FCS-LAPD model, the uncertainties of RESs are characterized using Gaussian Mixture Model (GMM). The required reserves are determined by frequency control parameters, and the reserve cost coefficients are adjusted properly to allocate the reserves according to the predicted power generation. Due to the nonlinearity of the frequency nadir constraint, a convex hull approximation method is employed to linearize it with guaranteed feasibility. The proposed FCS-LAPD is ultimately cast as an instance of quadratic programming and can be efficiently solved. Case studies on modified IEEE 24-bus system and a provincial power system in China are conducted to show the effectiveness of the proposed model.

The tight coupling of electric and heating systems would make it necessary to consider the coordinated planning of the energy facilities. The seasonal district heating network (DHN) reconfiguration can notably impact the planning results and offer a potential approach for better wind penetration. To address this issue, a coordinated planning of electric and heating (CPEH) model is proposed in this paper by considering the seasonally DHN reconfiguration, which is formulated as a mixed-integer linear programming problem. Considering the computation difficulties brought in by numerous operation scenarios involved in the CPEH problem, a scenario-oriented generalized Benders decomposition (S-BD) algorithm is proposed to solve the CPEH model with higher efficiency by generating feasibility and optimality cuts with a unified form. The numerical simulations and a practical case in Jilin, China, are simulated to demonstrate the efficiency of the S-BD and illustrate the potential benefits of DHN reconfiguration in enhancing wind energy penetration.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-, park-, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

A multi-energy virtual power plant can be defined as flexible cooperation of multi-energy distributed energy resources including heating and cooling devices that operates as a single entity and participates in the power markets. To exploit the potential of multi-energy distributed resources, this paper proposes a model for the aggregation of a multi-energy virtual power plant that participates in day-ahead energy and reserve markets. The reserve capacity brought by the multi-energy quick-start devices is considered in this paper, which adds binary variables to the recourse problem. The proposed model is an adjustable robust optimization problem that guarantees all possible deployment requests can be realized by the virtual power plant. Moreover, the optimal offering strategy is determined, and the total cost of the multi-energy virtual power plant is ensured to not exceed the optimal value of the objective function. A modified nested column-and-constraint generation algorithm is proposed for solving the adjustable robust optimization problem with mixed-integer recourse in specified precision efficiently compared with other KKT-based algorithms. The results from a case study of an actual industrial park demonstrate the satisfactory performance of the proposed model.