• Energy Research

Abstract: Due to the autonomous characteristic and heterogeneity of the individual agents in active distribution network (ADN) with multi-microgrids (MMG), this paper proposes a fully decentralized adjustable robust operation framework achieving the coordinated operation between ADN and MMG. The improved linear decision rules (LDRs) based microgrid adjustable robust operation model is proposed to reduce the solution conservatism in dealing with renewable energy uncertainty. The LDRs model is then reformulated as a computationally tractable solution such that the proposed adjustable robust extension of decentralized operation can handle renewable energy uncertainty while reducing the computation burden of decentralized optimization. Then, a tailored fast alternating direction method of multipliers algorithm with a predictor–corrector type acceleration step is developed to improve the convergence rate of decentralized optimization. The effectiveness of the proposed model is validated on a modified IEEE 69-bus distribution system with four microgrids.

Modern energy infrastructure has evolved into an integrated electricity and natural gas systems (IEGS), which often encompasses multiple geographically-diverse energy areas. This paper focuses on the decentralized adjustable robust operation problem for multi-area IEGS. Existing distributed algorithms usually require synchronization of all area subproblems, which is hard to scale and could result in under-utilization of computation resources due to the heterogeneity of local areas. To address those limitations, this paper proposes an asynchronous alternating direction method of multipliers (ADMM) based decentralized model for multi-area IEGS. This asynchronous decentralized structure only requires local communications and allows each area to perform local updates with information from a subset of but not all neighbors, where the individual areas' subproblems are solved independently and asynchronously. Meanwhile, the linear decision rules (LDRs)-based adjustable robust operation model is tailored to combine with the automatic generation control (AGC) systems to fully exploit its potential in dealing with renewable energy uncertainty. Numerical results illustrate the effectiveness of the proposed method.

The increasing application of voltage source converter (VSC) high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm -- Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers(ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to (VSC-MTDC) coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.

This paper introduces a distributed operational solution for coordinating integrated transmission-distribution (ITD) systems regarding data privacy. To tackle the nonconvex challenges of AC optimal power flow (OPF) problems, our research proposes an enhanced version of the Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN). This proposed framework incorporates a second-order correction strategy and convexification, thereby enhancing numerical robustness and computational efficiency. The theoretical studies demonstrate that the proposed distributed algorithm operates the ITD systems with a local quadratic convergence guarantee. Extensive simulations on various ITD configurations highlight the superior performance of our distributed approach in terms of convergence speed, computational efficiency, scalability, and adaptability.

Blending green hydrogen from renewable generations into the natural gas infrastructure can effectively mitigate carbon emissions of energy consumers. However, distributed hydrogen blending could lead to heterogeneous gas compositions across the network. The traditional nodal energy price scheme is designed for uniform gas composition, which cannot reflect the impacts of heterogeneous nodal gas composition and carbon emission mitigation. This paper proposes a novel nodal energy price scheme in hydrogen-blended integrated electricity and gas systems (H-IEGS). First, we propose a joint market-clearing model for H-IEGS, where the nonlinear physical properties of gas mixtures caused by heterogeneous gas compositions are characterized. The impacts of hydrogen blending on the carbon emission cost are also quantified. To retrieve the nodal energy price from this highly nonlinear and nonconvex optimization problem, a successive second-order cone programming (SSOCP) method is tailored to get the dual variables tractably. Considering the continuous market clearing process, a warm-start technique is proposed to provide initial reference points for the SSOCP to improve computation efficiency. Finally, an H-IEGS test case in Belgium and a large-scale practical case in Northwest China are used to validate the effectiveness of the proposed method.

AbstractThe clean Energy router based on advanced adiabatic compressed air energy storage (AA‐CAES) has the characteristics of large capacity, high efficiency and zero carbon emission which are an effective mitigation scheme for the integration of renewables and peak‐shaving and a new clean energy technology for storing energy in the world. A novel solar‐thermal‐assisted AA‐CAES (ST‐AA‐CAES) is proposed in this paper, integrating variable thermal energy storage to improve the system electric to electric (E2E) and round‐trip efficiency (RTE). The efficiency and exergy evaluation of ST‐AA‐CAES are carried out to determine the performance of ST‐AA ‐CAES. The results illustrate that E2E, RTE, and exergy efficiency can reach 56.4%, 95.5%, and 55.9%, respectively. Meanwhile, the details of exergy efficiency and destruction of each subsystem are demonstrated. Particle swarm optimization algorithm is applied to analyse the economy of optimally integrated energy systems which has the advantages of high accuracy, convenient implementation and fast convergence. The system can be applied in abundant solar energy resources area with high efficiency and multi‐energy supply capability.

This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually require synchronization of all subproblems, which could be hard to scale, resulting in the under-utilization of computation resources due to the subsystem heterogeneity in ITD systems. Moreover, the most commonly used distributionally robust individual chance-constrained dispatch models cannot systematically and robustly ensure simultaneous security constraint satisfaction. To address these limitations, this paper presents a novel distributionally robust joint chance-constrained (DRJCC) dispatch model for ITD systems via asynchronous decentralized optimization. Using the Wasserstein-metric based ambiguity set, we propose data-driven DRJCC models for transmission and distribution systems, respectively. Furthermore, a combined Bonferroni and conditional value-at-risk approximation for the joint chance constraints is adopted to transform the DRJCC model into a tractable conic formulation. Meanwhile, considering the different grid scales and complexity of subsystems, a tailored asynchronous alternating direction method of multipliers (ADMM) algorithm that better adapts to the star topological ITD systems is proposed. This asynchronous scheme only requires local communications and allows each subsystem operator to perform local updates with information from a subset of, but not all, neighbors. Numerical results illustrate the effectiveness and scalability of the proposed model.