Battery energy storage systems (BESS) are crucial for maintaining grid frequency stability, particularly with the increasing integration of intermittent renewable energy sources. However, energy delivered from BESS drifts over time due to asymmetric charging and discharging inefficiencies, posing significant challenges for effective frequency support. In this work, simulations revealed that energy drift accelerated early crossing of the state of charge (SOC) boundaries. A sensitivity analysis of asymmetric inefficiencies showed that improving charging and discharging consistency reduced energy drift and decreased frequency of energy transactions. C-rate constraints highlighted a trade-off between BESS safety and the rapidity of frequency support responses. Simplified dynamic droop control was shown to stabilize early-stage grid operations but required degradation-informed BESS control to mitigate the energy drift. The sizing of BESS showcased a trade-off between energy drift and cost. These findings offer valuable insights into designing safe and robust SOC management strategies for grid-connected BESS. Broadly, this work enhances fundamental understanding of energy drift arising from inherent battery degradation and its impact on the reliable energy storage systems for the stable and sustainable power grid.

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive load demand for incentivizing carbon alleviation. It first formulates the day-head distribution network scheduling model based on the second-order cone program (SOCP). The emission propagation and responsibility are analyzed from demand to supply to system emission. Considering the complex and implicit mapping of the SOCP-based scheduling model, the implicit theorem is leveraged to exploit the optimal condition of SOCP. The corresponding SOCP-based implicit derivation approach is proposed to calculate the DLMEs effectively in a model-based way. Comprehensive numerical studies are conducted to verify the superiority of the proposed method by comparing its calculation efficacy to the conventional marginal estimation approach, assessing its effectiveness in carbon alleviation with comparison to the average emission factors, and evaluating its carbon alleviation ability of reactive DLME.

This article presents a resilience assessment approach for hybrid ac/dc cyber-physical power system (CPPS), proposing a comprehensive assessment index called cascading failure recovery index (CFRI) that simultaneously considers the system scale and load level in the cascading failure recovery process. First, correlation characteristic matrix-based modeling framework is developed to capture the characteristics of multidimensional heterogeneous power systems, providing a clear description of the cyber-physical coupling network. Besides, the proposed CFRI incorporates cyber-physical coordinated attacks to assess the robustness of hybrid ac/dc power systems under different attack scenarios. The CFRI takes into account the number of nodes, branches, and load levels, enabling an accurate assessment of the disconnection degree and recovery capability of CPPS in case of cascading failures. Finally, simulation studies are conducted on a IEEE 39-bus power system modified with dc transmission lines to validate the effectiveness of the proposed method.

In this article, we propose an asynchronous distributed model predictive control (MPC) based droop control (AD-MDC) scheme to suppress fatigue load in doubly fed induction generator-based wind farms (WFs). To improve the fatigue load suppression performance, a droop control method is adopted in conjunction with an MPC-based WF control scheme to coordinate the active power of wind turbines (WTs). Within the predictive horizon, the generator speed sensitivity is calculated to formulate the MPC-based optimization problem, thereby achieving global optimal regulation of active power while tracking the dispatch command. For more effective control of WTs, an asynchronous distributed alternating direction method of multipliers is developed to minimize the fluctuations of fatigue load, while improving the time efficiency and information privacy protection. A WF with 40 × 5 MW WTs is established as a testing system to verify the effectiveness of the proposed AD-MDC scheme.

The knowledge about a network model is essential for control. However, the topology of wind farms (WFs) may be changed due to random off-grid events of wind turbines. This article develops a two-stage local method for WFs to achieve the Var/Volt coordinated optimization with consideration of topology changes, which provides an efficient solution when limited data are available for identification and control. In the first stage, considering the operational characteristics of WFs, a mixed integer optimization problem is formulated in each local controller to uncover the physical network topology. The problem is then solved and driven toward the optimum by an online recursive search algorithm. The proposed algorithm is intended to identify the physical network layout only using local measurements at no loss of accuracy. In the second stage, a completely decentralized Var/Volt control method is designed with the updated topological knowledge, aiming to achieve a fast Var response and to reduce voltage deviation caused by the stochastic wind power and voltage disturbance of external girds. Case studies in MATLAB/Simulink verify the robustness and effectiveness of the proposed method within a wide range of working conditions.

Integration of heat and electricity supply improves the overall energy efficiency and system operational flexibility. The renewable powered heat-electricity energy system is a promising way to set up residential energy supply facilities in remote areas beyond the reach of power system infrastructures. However, the volatility of wind and solar energy brings about the risk of supply inadequacy. This article proposes a data-driven robust method to quantify two measures of such a risk in the stand-alone renewable powered heat-electricity energy system. The uncertainty of renewable generation is modeled through a family of ambiguous probability distributions around an empirical one based on the Wasserstein metric; then, the probability of heat and electricity load shedding during a short period and related penalty cost are discussed. Through a polyhedral characterization of renewable power feasible region, the load shedding probability under the Wasserstein ambiguity set comes down to a linear program. With a piecewise linear optimal value function of the penalty cost, its expectation under the worst case distribution in the Wasserstein ambiguity set also gives rise to a linear program. The proposed method requires moderate information on renewable generation and makes full use of available data, whereas sustains computational tractability. The evaluation result is robust against the inaccuracy of renewable power distributions. Case studies demonstrate the effectiveness of the proposed approach.

An adaptive droop-based hierarchical optimal voltage control (DHOVC) scheme is proposed for voltage-source converter high-voltage-direct-current (VSC-HVdc) connected offshore wind farms (WFs). The wind turbines (WTs) and WF side VSC (WFVSC) are coordinated to minimize the voltage deviations of buses inside the WF from the nominal voltage and mitigate reactive power (Var) fluctuations of WTs. The model predictive control is used to improve the performance of the DHOVC scheme during a certain predictive horizon. A hierarchical solution method based on the alternating direction method of multipliers is developed to reduce the calculation burden of the central controller while improving the information privacy protection. During the predictive horizon, the WTs and WFVSC are coordinated to achieve the near global optimal performance without global information. A WF with 32 × 5MW WTs was used in the MATLAB/Simulink to test the proposed DHOVC scheme.

The optimal deployment of heterogeneous energy storage (HES), mainly consisting of electrical and thermal energy storage, is essential for increasing the holistic energy utilization efficiency of multienergy systems. Consequently, this article proposes a risk-averse method for HES deployment in a residential multienergy microgrid (RMEMG), considering the diverse uncertainties and multienergy demand-side management (DSM). Apart from the HES size and location planning, its optimal investment phase is also determined by maximizing the system equivalent daily profit (EDP) and minimizing the risk. To handle the system uncertainties from renewable energy sources, power demands, outdoor temperature, and residential hot water needs, the multistage adaptive stochastic optimization approach is utilized. Then, through the constraint linearization and stochastic scenario sampling, the original nonlinear deployment model is converted to a mixed-integer linear programming one and tested on an IEEE 33-bus distribution network based RMEMG. The effectiveness of the proposed method is verified by comparing it with the existing practices. The comparison results indicate that the proposed risk-averse deployment method can effectively increase the system EDP and more immune to the uncertainties. Besides, this method can be practically applied for the emerging RMEMGs, such as smart buildings, intelligent homes, etc., which get long-term DSM contracts.

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.

Evolving dynamics of modern power systems caused by high penetration of renewable energy sources increased the risk of failures and outages due to declining power system inertia. Large-scale wind farms must participate in frequency control that responds optimally in due time and adaptively in case of detecting power imbalance in the grid. Existing research studies have shown interest on stepwise inertial control (SIC) on wind turbines (WTs). However, the adequate power increment and time duration of WTs using SIC are the key questions that have not yet been fully addressed. This paper proposes an intelligent learning-based control system for WTs participation in frequency control, as well as for mitigating negative effects of the SIC. First, an appropriate optimization model for grid frequency control is defined. Then, the model is solved using lightning flash algorithm (LFA), imperialist competitive algorithm, and particle swarm optimization to control the WTs in a wind farm. The obtained dataset by LFA are applied to an artificial neural network that is trained with the Levenberg-Marquardt algorithm and LFA. The proposed control system optimally adjusts the power increment and duration time of participation for each WT in the farm. Analyses on a 100 MW wind farm has been integrated into the IEEE 9-bus system and experimental tests have proved the efficacy of the proposed approach.

The increasing penetration of photovoltaic (PV) generators has led to a reduction in the effectiveness of existing strategies for restoring the power distribution network. This article proposes a dynamic robust restoration (DRR) framework for the recovery of outage power considering uncertain PV outputs and demands. This framework is presented in two subsequent steps. In the first step, optimal decisions regarding the network configurations are generated. The second step then computes the modified dynamic Distflow equations and constraints under consideration of the worst operating conditions over the associated uncertainty sets with the aim of maximizing the recovery of outage power. The DRR model is formulated as a bilevel mixed-integer linear programming problem. A decomposition algorithm in a master-sub structure is used to solve the resulting system. The results of case studies show that the proposed DRR model yields obvious advantages over the existing deterministic dynamic restoration model in terms of robustness against system uncertainties.

Understanding potential behaviors of attackers is of paramount importance for improving the cybersecurity of power systems. However, the attack behaviors in existing studies are often modeled statically on a single snapshot, which neglects the reality of a dynamically time-evolving power system. Accordingly, a dynamic cyber-attack model with local network information is proposed to characterize the typical data injection attack with the integration of potential dynamic behaviors of an attacker. The proposed model collaboratively alters the meter measurement in a stealthy way to illegally contaminate the system state, thus posing severe threats to cyber physical power systems. We then develop a novel anomaly detection countermeasure from the perspective of state estimation to effectively recognize the dynamic injection attack. In this countermeasure, an interval state forecasting method is proposed to approximate the possible largest variation bounds of each state variable based on a worst-case analysis considering the forecasting uncertainties of renewable energy sources, electric loads, and network parameter perturbations. In addition, the kernel quantile regression is introduced and implemented to formulate the uncertainties in renewable energy and electric load forecast as a series of confidence intervals. When any state variable falls outside its preforecasted intervals, the proposed countermeasure detects the anomaly and sets an alarm condition indicating the possibility of data contamination. Finally, the results from our extensive studies on several IEEE standard test systems have been presented to demonstrate the feasibility of the dynamic attack and the effectiveness of the detection countermeasure.

This paper proposes a distributed multienergy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas, and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multimicrogrid problems.

Generation expansion planning (GEP) is the problem of finding the optimal strategy to plan the construction of new generation while satisfying technical and economical constraints. In the deregulated and competitive environment, large-scale integration of wind generation (WG) in power system has necessitated the inclusion of more innovative and sophisticated approaches in power system investment planning. A bi-level generation expansion planning approach considering large-scale wind generation was proposed in this paper. The first phase is investment decision, while the second phase is production optimization decision. A multi-objective PSO (MOPSO) algorithm was introduced to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of the proposed bi-level planning approach and the MOPSO algorithm have been verified by a numerical test system.

This paper describes a coordinated control scheme of battery energy storage system (BESS) and distributed generations (DGs) for electric distribution grid operation. The BESS is designed to stabilize frequency and voltages as a primary control after the electric distribution system enters into the islanding operation mode, while the centralized joint load frequency control (CJLFC) utilizing DGs handles the secondary frequency regulation. The BESS with the associated controllers has been modelled in Real-time digital simulator (RTDS) in order to identify the improvement of the frequency and voltage response. The modified IEEE 9-bus system, which is comprised of several DG units, wind power plant and the BESS, has been employed to illustrate the performance of the proposed coordinated flexible control scheme using RTDS in order to verify its practical efficacy.