With the widespread application of combined heat and power (CHP) units, the economic dispatch of integrated electric and district heating systems (IEHSs) has drawn increasing attention. Because the electric power system (EPS) and district heating system (DHS) are generally managed separately, the decentralized dispatch pattern is preferable for the IEHS dispatch problem. However, many common decentralized methods suffer from the drawbacks of slow and local convergence. Moreover, the uncertainties of renewable generation cannot be ignored in a decentralized pattern. Additionally, the most commonly used individual chance constraints in distributionally robust optimization cannot consider safety constraints simultaneously, so the safe operation of an IEHS cannot be guaranteed. Thus, distributionally robust joint chance constraints and robust constraints are jointly introduced into the IEHS dispatch problem in this paper to obtain a stronger safety guarantee, and a method combined with Bonferroni and conditional value at risk (CVaR) approximation is presented to transform the original model into a quadratic program. Additionally, a dynamic boundary response (DBR)-based distributed algorithm based on multiparametric programming is proposed for a fast solution. Case studies showcase the necessity of using mixed distributionally robust joint chance constraints and robust constraints, as well as the effectiveness of the DBR algorithm.

When large-scale, uncertain, centralized, and distributed renewable energy sources are connected to a power system, separate dispatch of the transmission power system (TPS)and the active distribution network (ADN) will compromise the network and frequency security of the system. To address these problems, this paper proposes a frequency-constrained coordinated stochastic economic dispatch (FC-CSED) model for an integrated transmission and distribution (ITD) system. In this model, the dynamic frequency and network security constraints of the ITD system are constructed, and joint chance constraints are adopted to handle the uncertainty. Then, the control parameters of inverter-based resources, the base point power, and the regulation reserve of all dispatchable resources in the ITD system are jointly optimized to minimize operating cost. TPS and ADNs can deliver base point power bidirectionally and provide frequency regulation support bidirectionally, which extends the existing reserve assumption in ITD dispatch and enhances the operational security of the ITD system. Moreover, based on the alternating direction of the multiplier algorithm, a two-layer distributed optimization framework is proposed to solve the FCCSED model. Case studies show the FC-CSED model can fully utilize the potential of multiple regulation resources to improve the security performance of the ITD system, and TPS and ADNs can be coordinated efficiently through the proposed distributed optimization framework.

The issues of uncertainty and frequency security become significantly serious in power systems with the high penetration of volatile inverter-based renewables (IBRs), which makes it necessary to consider the uncertainty and frequency-related constraints in the economic dispatch (ED) programs. However, existing ED studies rarely proactively optimize the control parameters of inverter-based resources related to fast regulation (e.g., virtual inertia and droop coefficients) in cooperation with other dispatchable resources to improve the system frequency security and dispatch reliability. This paper proposes a joint chance-constrained economic dispatch model that jointly optimizes the frequency-related inverter control, the system up/down reserves, and base-point power for the minimal total operational cost. In the proposed model, multiple dispatchable resources, including thermal units, dispatchable IBRs, and energy storage, are considered, and the (virtual) inertias, the regulation reserve allocations, and base-point power are coordinated. To ensure the system reliability, the joint chance-constraint formulation is also adopted. Additionally, since the traditional sample average approximation (SAA) method imposes a huge computational burden, a novel mix-SAA (MSAA) method is proposed to transform the original intractable model into a linear model that can be efficiently solved via commercial solvers. The case studies validate the satisfactory efficacy of the proposed ED model and demonstrate that the MSAA can save nearly 90% calculation time compared with the traditional SAA.

Coordinated operation of power, heating, and gas systems in an integrated energy system (IES) can effectively improve the energy systems’ economy, safety, and reliability. However, the natural gas system is dynamic and is modeled by complex nonconvex constraints, which makes the natural gas system dispatch model difficult to solve effectively. This poses a serious challenge to the optimal dispatch of the IES with a natural gas system. Inspired by a recent novel rank minimization algorithm (NRMA), this paper proposes a modified rank minimization algorithm (MRMA), which overcomes the infeasibility issue of the NRMA and can efficiently solve the dynamic natural gas system (DNGS) dispatch problem. Moreover, binary search and acceleration approaches are proposed to improve the MRMA’s computational efficiency, surpassing the NRMA and the conventional interior point method. Numerical tests demonstrate that MRMA can solve DNGS dispatch problems much more efficiently than conventional methods.