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Combined heat, and power dispatch promotes interactions, and synergies between electric power systems, and district heating systems. However, nonlinear, and nonconvex heating flow imposes significant challenges on finding qualified solutions efficiently. Most existing methods rely on constant flow assumptions to derive a linear heating flow model, sacrificing optimality for computational simplicity. This paper proposes a novel convex combined heat, and power dispatch model based on model simplification, and constraint relaxations, which improves solution quality, and avoids assumptions on operating regimes of district heating systems. To alleviate mathematical complexity introduced by the commonly used node method, a simplified thermal dynamic model is proposed to capture temperature changes in networked pipelines. Quadratic, and polyhedral relaxations are then applied to convexify the original problem with quadratic equality, and bilinear constraints. Furthermore, an adaptive solution algorithm is developed to successively reduce the relaxation area based on sequential bound tightening, which improves solution optimality with desirable computational efficiency. The proposed method is verified on a distribution-level, and a transmission-level integrated electricity, and heat systems, compared to constant-flow-based solutions, and iterative algorithms. [ABSTRACT FROM AUTHOR]

Combined heat and power dispatch (CHPD) is utilized for energy-efficient coordination of integrated electricity and heat systems. By incorporating district heating networks (DHNs) into CHPD, the thermal storage capability of network pipelines can be exploited to increase the operational flexibility for high penetration of renewable energy. This article proposes a flexibility evaluation method based on a generalized thermal storage model to systematically characterize and quantify the flexibility of DHNs in CHPD. In particular, systematic flexibility metrics are introduced to define the parameters of the proposed thermal storage model, including heat ramping capability, heat input limits, and heat storage capacities. A direct quantification method is proposed to explicitly derive these metrics without extensive simulations required in traditional methods. Besides, four different control modes of district heating systems are identified and modeled according to the controllability of mass flow rates and supply temperature at heat sources. A simplified CHPD model is developed based on sequential linear programming and a lumped heat loss model to enhance the computational efficiency. Comprehensive case studies validate the effectiveness of the proposed method in evaluating the flexibility of DHNs in CHPD. It is demonstrated that the flexibility of DHNs in CHPD can be exploited as far as possible in the control mode with adjustable mass flow and supply temperature. [ABSTRACT FROM AUTHOR]

High penetration of distributed renewable energy introduces significant uncertainties to active distribution networks. Optimal control methods accounting for inherent uncertainties are needed to facilitate economic and reliable operation of active distribution networks. This paper proposes a stochastic receding horizon control method based on modified stochastic model predictive control framework to integrate high penetration of distributed generation. Multiple controllable resources are jointly optimized over a finite prediction horizon while ensuring relevant security restrictions. The simplified Z-bus sensitivity for active distribution networks is developed for computationally efficient estimation of system nonlinearity with high accuracy, and is combined with the sequential linear programming to iteratively derive the linear state space model for compensation of cumulative modeling errors. Furthermore, the voltage limitations are reformulated as chance constraints to indicate the probabilistic reliability index of voltage qualification rate, and achieve tradeoffs between cost reduction and voltage regulation. The affine-disturbance feedback control policy is leveraged here to enforce close-loop control performance and analytically transform intractable chance constraints into second-order cone constraints. Comprehensive case studies based on 33-bus and 123-bus distribution systems are carried out to demonstrate the capability and effectiveness of the proposed approach in terms of modeling accuracy, control performance, cost reduction, and method scalability. The proposed approach can effectively enforce voltage regulation against uncertainties with the prescribed probability level. Control costs and constraint violation can be reduced compared with deterministic model predictive control and open-loop control strategies. [ABSTRACT FROM AUTHOR]