• Energy Research

In emerging distribution systems with a proliferation of distributed energy resources (DERs) and flexible demand assets, operation characters of the unbalance network and voltage regulation devices need to be accurately addressed for ensuring the secure and economic operation. This paper focuses on the modeling and solution approach of AC optimal power flow (ACOPF) for unbalanced distribution systems with DERs and voltage regulation transformers (VRT). The ACOPF problem is formulated as a chordal relaxation based semidefinite programming (SDP) model, and a tighter convexification model of VRTs is proposed for mitigating solution inexactness. Analytical conditions are presented and proved to determine whether global optimal solution to the original ACOPF problem can be retrieved from solutions of the chordal relaxation based SDP model. Numerical studies on modified IEEE 34-bus and 8500-node systems show that the proposed approach presents a better computational performance as compared to rank relaxation based SDP approaches and general nonlinear solvers.

This paper investigates a Local Strategy-Driven Multi-Agent Deep Deterministic Policy Gradient (LSD-MADDPG) method for demand-side energy management systems (EMS) in smart communities. LSD-MADDPG modifies the conventional MADDPG framework by limiting data sharing during centralized training to only discretized strategic information. During execution, it relies solely on local information, eliminating post-training data exchange. This approach addresses critical challenges commonly faced by EMS solutions serving dynamic, increasing-scale communities, such as communication delays, single-point failures, scalability, and nonstationary environments. By leveraging and sharing only strategic information among agents, LSD-MADDPG optimizes decision-making while enhancing training efficiency and safeguarding data privacy—a critical concern in the community EMS. The proposed LSD-MADDPG has proven to be capable of reducing energy costs and flattening the community demand curve by coordinating indoor temperature control and electric vehicle charging schedules across multiple buildings. Comparative case studies reveal that LSD-MADDPG excels in both cooperative and competitive settings by ensuring fair alignment between individual buildings’ energy management actions and community-wide goals, highlighting its potential for advancing future smart community energy management.

The energy-saving dispatch is becoming one of the most important smart grid components in China, which asks for both generation and demand side resources’ participation. In smart electricity grid, the uncertainties related with variable renewable energy outputs on the generation side, as well as the deviations between the scheduled demand response (DR) resources and their actual availabilities on the demand side should be simultaneously taken care of. In this paper, a new energy-saving dispatch problem is studied considering the uncertainties from both generation and demand sides. A bi-level optimization model is established to address the generation and demand side’s interactions considering economic dispatch of thermal units and load control schemes of end users in the new energy-saving dispatch problem. The upper-level minimizes the electric transmission company’s power generation and carbon emission costs while maintaining the system reliability, and the lower-level minimizes the expected total load dispatch cost paid by the electric distribution company to DR providers and the demand side emission cost. An iterative algorithm is adopted to solve the proposed bi-level model. In addition, improved NSGA-II method is used to solve the lower-level multi-objective optimization problem for seeking optimal compromise solution. Numerical case studies on the practical energy-saving dispatch problem of Guangdong Province in China illustrate the effectiveness of the proposed approach.

The growing integration of renewable energy sources, especially offshore wind (OSW), is introducing frequency stability challenges to electric power grids. This paper presents a novel hybrid deloading control strategy that enables modular multilevel converter (MMC)-based wind energy conversion systems (WECSs) to actively contribute to grid frequency regulation. This research investigates a permanent-magnet synchronous generator (PMSG)-based direct-drive configuration, sourced from the International Energy Agency’s (IEA’s) 15 MW reference turbine model. Specifically, phase-locked loop (PLL)-free grid-forming (GFM) control is employed via the grid-side converter (GSC), and DC-link voltage control is realized through the machine-side converter (MSC), both of which boost the energy support for the integrated AC grid’s frequency stability. This control strategy combines the benefits of over-speeding and pitch control modes, facilitating smooth transitions between different modes based on real-time wind speed measurements. In addition, the practical challenges of MMCs, such as circulating currents and capacitor voltage imbalances, are addressed. Numerical simulations under varying wind speeds and loading conditions validate the enhanced frequency regulation capability of the proposed approach.

The emerging distribution system with a proliferation of distributed energy resources and flexible demand assets is expected to experience a restructuring process, just as what has been happening in the transmission system. This paper introduces the distribution locational marginal price (DLMP) as an effectively economic signal to quantify marginal cost for supplying next incremental loads at different phases of individual nodes. DLMP is calculated by solving the unbalanced ac optimal power flow (ACOPF) problem of distribution systems, with the objective of minimizing system operation cost. Indeed, in order to derive effective DLMPs, global optimal solution to the non-convex unbalanced ACOPF problem with a zero duality gap needs to be obtained. This paper solves the unbalanced ACOPF problem via the moment relaxation-based semidefinite programming (SDP) model. System sparsity is explored to accelerate the computational performance. In addition, a hierarchical approach is proposed to recover a good enough feasible solution to the original ACOPF, when the sparse moment relaxation-based SDP model is inexact. Numerical case studies on a modified IEEE 34-bus system evaluate the effectiveness and validity of the proposed approach. DLMP-based revenue adequacy of the distribution system is also analyzed.

Network reconfiguration has long been used by distribution system operators to achieve certain operation objectives such as reducing system losses or regulating bus voltages. In emerging distribution systems with a proliferation of distributed energy resources (DERs), co-optimizing network topology and DERs’ dispatches could further enhance such operational benefits. This paper focuses on the optimal network reconfiguration problem of distribution systems via an unbalanced ac optimal power flow framework, which rigorously addresses operation characters of unbalanced network, DERs, and voltage regulators (VRs). Two VR models with continuous and discrete tap ratios are studied and compared. The proposed co-optimization problem is formulated as a mixed-integer chordal relaxation-based semidefinite programming model with binary variables indicating line-switching statuses and tap positions. Several acceleration strategies by studying the structure of distribution networks are explored for reducing the number of binary variables and enhancing the computational performance. Case studies on modified IEEE 34-bus and 392-bus systems illustrate the effectiveness of the proposed approach.

Microgrid is regarded as the future power system configuration, which can be operated by connecting to the bulk power grid, as well as in stand-alone mode for serving microgrid loads during failures of upstream grid. This paper proposes a business model for operating the community-based multi-party microgrid, which involves critical loads and generators of multiple owners. Different from most operational microgrids with single owner of critical loads and on-site generators, the community-based multi-party microgrid discussed in this paper presents different structure and thus unique operating goals. An iterative bi-level model is proposed to simulate the interaction between the community microgrid operator (named as mini-ISO) and multiple parties for deriving good enough market clearing results during the microgrid’s normal operation status. On the other hand, a centralized energy management model is proposed to simulate the microgrid operation during major disruption events with limited generation resources and competing needs from multiple owners’ critical loads. Numerical simulations evaluate the effectiveness of the proposed operation framework for the community-based multi-party microgrid.

This paper studies the optimal operation of internet data centers (IDCs) in microgrid environments. The proposed model is to minimize the IDC microgrid operation cost, which consists of the operation cost of onsite generations, cost of purchasing power from the main grid, cost due to the repeated charging and discharging of onsite energy storage system (ESS), and the cost due to IDC workload shedding. Both preventive and corrective operation solutions, including power purchase from the main grid, onsite generation dispatch, as well as ESS and IDC workload scheduling are provided for provisioning IDC workloads while considering credible islanding scenarios of the IDC microgrid. Demand response capability of the IDC microgrid is achieved through the proposed optimal solution. The use of ESS for facilitating electricity load shifting is further analyzed via a sensitivity study for minimizing the operation cost of IDC microgrid. Numerical simulations demonstrate the effectiveness of the proposed optimal operation model for IDC microgrid.