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Abstract Reuse and recycling of retired electric vehicle (EV) batteries offer a sustainable waste management approach but face decision-making challenges. Based on the process-based life cycle assessment method, we present a strategy to optimize pathways of retired battery treatments economically and environmentally. The strategy is applied to various reuse scenarios with capacity configurations, including energy storage systems, communication base stations, and low-speed vehicles. Hydrometallurgical, pyrometallurgical, and direct recycling considering battery residual values are evaluated at the end-of-life stage. For the optimized pathway, lithium iron phosphate (LFP) batteries improve profits by 58% and reduce emissions by 18% compared to hydrometallurgical recycling without reuse. Lithium nickel manganese cobalt oxide (NMC) batteries boost profit by 19% and reduce emissions by 18%. Despite NMC batteries exhibiting higher immediate recycling returns, LFP batteries provide superior long-term benefits through reuse before recycling. Our strategy features an accessible evaluation framework for pinpointing optimal pathways of retired EV batteries.

Abstract Meeting the power demand from the transmission system operator is an important objective for power dispatch, which introduces a power supply-demand equality constraint coupling all the wind turbines among the wind farm into the optimization problem. For a large-scale wind farm, processing the global equality constraint in a centralized or distributed framework is time-consuming and computationally complex. Here we considered the fast and localized execution issue of the power optimal dispatch problems. A completely decentralized dynamic system was designed to optimize power flow while satisfying the electricity supply constraints. A voltage optimization problem with the global power constraints was decoupled into local wind turbine controllers based on the node-dependence nature, which is an inherent characteristic of wind farms and was fitted to the power sensitivity matrix in this paper. The local optimization problem was solved iteratively using the gradient projection method, and the system converged linearly to the equilibrium point. The simulations for the case studies performed in Simulink demonstrate that the proposed method achieves a near-global optimal performance using only local measurements.

The random fluctuations of wind energy and external grid voltage disturbances can both lead to serious voltage fluctuations and voltage deviations in the wind farm (WF). Voltage/reactive power control is an effective way to improve the voltage stability of WF. The existing research is based on complex physical models with prior parameter information, and the accuracy and calculation speed of the WF model are difficult to guarantee. To address this issue, this paper explores a decentralized optimal voltage control method for WF with deep learning-based (DL) data-driven modeling (DL-DOVC). A hybrid convolutional neural network (CNN)-Transformer architecture is proposed to establish the data-driven model of WF, leveraging its enhanced capabilities for extracting time series correlation, to learn complex patterns and dynamics from time series data. Then, we develop a fully decentralized voltage control method for WF to regulate the terminal bus voltage of wind turbines (WTs) within a feasible range. A DL-based data-driven predictive controller is specially designed to solve the established data-driven voltage optimization problem, it eliminates the necessity for frequent manual model maintenance and is suitable for real-time application. Additionally, the difficulty of WF decentralized optimal voltage control arises from the inability of local controllers to fully consider the impact of all non-local WTs on the local WT states. By designing the DL-based auxiliary state-feedback controller, the effects of non-local WTs are implicitly considered in the auxiliary feedback control law. A WF with 32*6.25 MW WTs is used to test the proposed DL-DOVC method. Simulation results show that the proposed DL-based model can efficiently learn and predict the WF dynamics under different operating scenarios. The near-global optimal voltage control performance is achieved only with local measurements by employing the DL-DOVC method.

This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch. The proposed MF-PFVC method is based on a convenient combination of direct predictive torque control (PTC) and model free predictive control techniques. First, the voltage source inverter (VSI) vector control of N ∗ 3-Phase PMSM is determined by the three-segment stator windings structure. Then, the parameter mismatch sensitivity of the N ∗ 3-Phase PMSM drive system is analyzed based on the conventional PTC. Furthermore, a novel MF-PFVC method with multivariable unknown disturbance integral terminal sliding mode observer (SMO) is proposed for N ∗ 3-Phase PMSM drive, which can effectively enhance robustness against the parameters mismatch. Finally, compared with the conventional PTC, the simulation and experimental results of MF-PFVC method illustrated improved stator flux control performance concerning lower stator flux ripple as well as reduced torque ripple while it can eliminate the influence of parameter mismatch.

Summary: Prioritizing electric power system decarbonization is crucial for meeting global carbon neutrality targets. However, the role of energy technology cost reduction driven by innovation in advancing carbon neutrality in the electric power system has not been well studied. To fill this gap, an integrated investment planning and operation model is developed to simulate the carbon neutral pathway in the electric power system over a 30-year period from 2020 to 2050. The learning curves with different learning rates are incorporated into the model to represent different energy technology innovation scenarios. According to our results, the advanced innovation scenario is projected to achieve carbon neutrality in the electric power system five years earlier compared with the conservative innovation scenario, with a cost savings of 465 billion CNY. In addition, both inter-regional and intra-regional collaboration facilitate the achievement of carbon neutrality in the electric power system at a reduced cost. Science for society: Reducing carbon emissions and achieving carbon neutrality is a critical global challenge. Previous studies have formulated pathways for energy transition in the electric power system to meet carbon reduction targets. However, many of them overlook the role of energy technology cost reduction driven by innovation in advancing carbon neutrality in the electric power system. In this study, an integrated investment planning and operation model is developed to simulate the transition pathway in the electric power system from 2020 to 2050 toward carbon neutrality under different innovation scenarios. Our results reveal that carbon neutrality can be achieved earlier and more cost effectively in the advanced energy technology innovation scenario. Furthermore, expanding both inter-regional and intra-regional transmission capacities can facilitate the achievement of carbon neutrality in the electric power system at a reduced cost.

Service restoration (SR) is essential for resilience enhancement of distribution systems (DSs) when natural disasters occur. However, a restored DS may face potential outage risks caused by subsequent contingency during the prolonged event. To improve the risk-resistance of SR strategies, this paper proposes a microgrid-based SR method to continuously supply critical loads when considering subsequent contingency. The microgrids are proactively formed to mitigate the influence of subsequent contingencies, which can effectively reduce the load shedding and wind power curtailment during the SR process. To deal with uncertainties of subsequent contingency and wind power, a two-stage robust model is established. Moreover, an improved uncertainty set is established to further reduce the conservatism of robust optimization. Specially, the line interruption probability is considered when constructing the Shannon entropy-based uncertainty set of subsequent contingencies, and the spatial–temporal correlations of wind power is considered when formulating the budget-constrained uncertainty set. Then, the robust model is solved by column-and-constraint generation algorithm efficiently. Numerical results demonstrate that the proposed SR method can effectively enhance the risk-resistance of SR strategy and reduce the conservatism while ensuring the robustness of solutions.

The uncertainty and correlation of energy prices pose challenges to the planning and operation of park-level integrated energy systems. Especially, the extreme scenarios in both electricity and natural gas prices would result in significant economic losses, which have not been fully studied in the planning of park-level integrated energy systems. In this regard, this paper proposes a conditional-value-at-risk-based planning method for the park-level integrated energy system that considers both the extreme scenarios of energy prices and the correlation between the electricity price and natural gas price. First, the Gaussian mixture model is used to model the probability distribution function of the electricity price and natural gas price with high accuracy. Then, the Frank-Copula function is adopted to model the correlation between energy prices in different energy markets. Furthermore, a novel scenario generation method preserving extreme scenarios of energy prices is proposed, where extreme price scenarios are identified in the scenario generation process and subsequently preserved in scenario reduction. Finally, a conditional-value-at-risk-based planning model is proposed to optimize the overall investment and operation costs of the park-level integrated energy system, where the operational risks introduced by renewable generation and energy markets are effectively handled by the conditional value at risk. Case studies demonstrate that compared with the model without the consideration of extreme scenarios of energy prices, the potential risk loss of the proposed model is reduced by 3% in the same generated scenarios. The proposed model is more beneficial in enhancing the system’s resilience to the risk.

In this study, a novel robust deadbeat predictive current control algorithm using voltage coefficient matrix update (DPCC-VCMU) is proposed for permanent magnet synchronous motor (PMSM), which can eliminate the influence of model parameter mismatch. First, the parameter sensitivity of conventional deadbeat predictive current control (DPCC) is discussed, and the multiple parameter error online identifiers are designed based on an improved Adaline neural network (ANN) weight adjustment algorithm. The novel identification structure with parameter mismatch error as the weight of neural network is then proposed to enhance the current tracking robustness under model parameter mismatch. Moreover, the parameter error identification value is used to update the coefficient matrix of the control voltage and predict the current value of next moment simultaneously. Finally, compared with conventional DPCC method, the simulation and experimental results verify that the proposed DPCC-VCMU-algorithm can significantly improve the current response accuracy by using voltage coefficient matrix update, while achieving lower torque ripple and smaller current harmonics.

This paper proposes a new real-time distributed voltage control (RTDVC) scheme for medium and low voltage (MV&LV) distribution networks (DNs) based on the analytical target cascading (ATC) method. In this scheme, photovoltaic (PV) units at the LV level, on-load tap changer (OLTC) and distributed generation (DG) clusters at the MV level are optimally coordinated to regulate the bus voltages in MV&LV DNs to be close to the nominal value while minimizing the number of operations of OLTC and smoothing the reactive power outputs of DGs. Based on the ATC method, the large-scale strongly coupled voltage control problem of MV&LV DNs is decomposed into several subproblems while guaranteeing the optimality of the primal voltage optimization problem. The decomposed subproblems are iteratively solved in parallel in the MV and LV controllers, which achieves optimal voltage control in a distributed manner and effectively reduces the computation burden. An MV&LV test system with two 20 kV feeders and 39 0.4 kV LV grids was simulated to demonstrate the control performance of the proposed distributed voltage control scheme.

Abstract The high‐order nonlinear complex characteristics of traditional static wake model (SWM) restrict the fast optimization of wake effect. Therefore, this paper introduces a dynamic wake model (DWM) to describe the time‐varying wake characteristics of wind turbines (WTs) with low computational cost. The traditional SWM is linearized to derive the wake wind speed sensitivity coefficients, which represents the sensitivity of wake wind speed deficit with respect to the active power reference. Considering the natural propagation characteristics of wake effect, a wake delay function is added to realize the future wind speed prediction of different locations of the wind farm. And a joint control strategy of wake and active power based on model predictive control (MPC) is proposed to optimize the power contribution of each WT to minimize wake effects, and maximize total available power. Thus, the increased available power is able to satisfy the power demand from Transmission System Operator (TSO) for ancillary services and enhance power support capability. The control performance of the proposed control strategy is evaluated by simulations under constant and varying incoming wind speed.

Abstract Flexible combined cooling, heating, and power (CCHP) systems are effective in integrating wind sources. As an attractive, clean, and large‐scale energy storage technique, the advanced adiabatic compressed air energy storage (AA‐CAES) can store and generate both electricity and heating, and also provide cooling during expansion under certain conditions. Although AA‐CAES has immense potential in multi‐energy supply systems, CCHP dispatch with AA‐CAES and wind power generation (WPG) is yet to be systematically studied. In this study, the economic dispatch of an AA‐CAES system equipped with WPG is addressed. The AA‐CAES system is comprehensively modelled by considering its thermal characteristics, air‐temperature changes due to heating exchange, air storage constraint, and other factors, particularly the heat supply to the air for expansion, which is a key factor that influences the cooling supply. Subsequently, the cooling, heating, and power of the AA‐CAES system are dispatched to minimise the operating cost under different supply modes. In conclusion, the proposed method is demonstrated using an integrated energy system in an industrial park, and the operation cost of the AA‐CAES system is minimised. The numerical results demonstrate that the participation of AA‐CAES in CCHP dispatch can curtail WPG and reduce operation costs. The economics of the different supply modes of AA‐CAES are also discussed.

Increasing intermittent renewable energy sources (RESs) intensifies the imbalance between demand and generation, entailing the diversification of the deployment of electrical energy storage systems (ESSs). A large-scale biogas plant (LBP) installed with heating devices and biogas energy storage (BES) usually exhibits a storage-like characteristic of accommodating an increasing penetration level of RES in rural areas, which is addressed in this paper. By utilizing the temperature-sensitive characteristic of anaerobic digestion that enables the LBP to exhibit a storage-like characteristic, this paper proposes a bi-level energy trading model incorporating LBP and demand response aggregator (DRA) simultaneously. In this model, social welfare is maximized at the upper level while the profit of DRA is maximized at the lower level. Compared with cases only with DRA, the results show that the proposed model with the LBP improves the on-site accommodation capacity of photovoltaic (PV) generation up to 6.3%, 18.1%, and 18.9% at 30%, 40%, and 50% PV penetration levels, respectively, with a better economic performance. This nonlinear bi-level problem is finally recast by a single-level mathematical program with equilibrium constraints (MPEC) using Karush-Kuhn-Tucker (KKT) conditions and solved by the Cplex solver. The effectiveness of the proposed model is validated using a 33-bus test system and a sensitivity analysis is provided for analyzing what parameter influences the accommodation capacity most.

In this study, a coordinated voltage control strategy based on model predictive control (MPC) is proposed for offshore radial DC-connected wind farms. Two control modes are designed in this strategy. In the economic operation mode, the wind farm controller generates optimal active power references as well as bus voltage references of medium-voltage collector for DC-connected wind turbine (DCWT) systems and high-voltage DC/DC converters, where the goal is to minimize power losses inside the wind farm and ensure that voltages are within a feasible range, all while tracking the power references. In the voltage control mode, the main control objective for the wind farm controller is to minimize voltage deviations from the rated voltage. With the MPC, the control objective and operation constraints can be explicitly represented in the optimization problem while considering the dynamic response of the DCWT system. In addition, a sensitivity coefficient calculation method for radial DC-connected wind farms is developed to improve computational efficiency. Finally, DC-connected wind farms with 20 wind turbines are used to demonstrate the performance of the proposed strategy.

Abstract To protect the environment and reduce carbon emissions, renewable power generation has been growing rapidly during the past decade. Renewable energy resources are sometimes far away from the main grid, leading to expensive grid‐connection transmission lines. Deploying on‐site energy storage can smooth the output power and help to reduce the renewable power spillage and the requirement of transmission line capacity. This paper presents a method to coordinately size on‐site energy storage and grid‐connection transmission line for a remote renewable power plant, minimising the total investment cost subject to the constraint of renewable curtailment risk. Through an optimal operation model, the renewable curtailment is proven to be a piecewise affine function of capacity parameters and renewable power generation, and a linear programming‐based algorithm is proposed to generate an approximate expression. A distributionally robust optimisation model is proposed to determine the sizes. The renewable generation uncertainty is modelled by a Wasserstein‐metric‐based ambiguity set containing probability distributions around the empirical distribution constructed from the historical data. The utilisation rate is ensured in the worst‐case distribution. The sizing problem is transformed into a tractable linear program. The case study demonstrates the effectiveness of the proposed method.

Abstract The 9th IET Renewable Power Generation Conference took place online in March 2021, having originally been intended to take place in Dublin, Ireland some months earlier. A major motivation for the conference was the recognition of the challenges and possibilities arising from increasing renewable shares in many countries, and the critical need for cost‐effective, reliable, and robust solutions as part of the ongoing transformation of our power and energy systems. Despite the restrictions of the pandemic, approximately 70 papers were successfully presented at the conference across 15 oral and poster sessions, covering a wide range of themes including low inertia power systems, wind power plant modelling and control, renewable energy forecasting, distribution network operation and long‐term planning, power system protection, electricity markets and system services etc. Subsequent to the conference, a number of the authors of the presented papers were invited to submit an extended version of their papers to a special issue of the IET RPG journal. Ultimately, after a thorough peer review process, 16 papers were accepted for the special issue, which have been grouped here under five main themes: electrical network operation and planning; power electronic converter control systems; flexibility/frequency support measures and electricity markets; power system security, protection and monitoring; and tidal stream energy.

The N-1 security principle is a key principle for distribution operation. However, it is not taken into account in modern distribution network reconfiguration models, which cannot satisfy security demands. And in order to solve this fundamental problem, this paper proposes a new reconfiguration method for distribution network considering N-1 security constraints. Firstly, distribution system security region are introduced to analyze real-time security as well as sub-concepts of security boundary and security distance are analyzed. Secondly, distribution feeder reconfiguration model is introduced to minimize the net loss considering N-1 security operation constraints. Thirdly, the model is solved by the improved genetic algorithm. Finally, the model of this paper is compared to the existing model and the results show that the reconfiguration strategy can satisfy the N-1 security principle and is better than modern models considering security of power systems, which prove that the proposed model and solving method are correct and effective.

The distribution system is normally equivalent as a dynamic power injected at the interconnected transmission bus. This cannot unlock the potential benefits of transmission–distribution interaction. To tackle this problem, this paper proposes a distributed stochastic optimization approach to simultaneously determine distribution system operation and transmission system planning. Uncertainties in the distribution and transmission systems are modelled using numbers of typical scenarios. The original problem is decomposed into the upper transmission network planning level and the lower distribution network operation one. Both two levels are linearized using a piecewise linearization approach to guarantee the convergence properties of the algorithm. Simulation results investigated on the integrated T24D9 system testify the high performance of the presented distributed framework. With the coordination of distribution systems, the transmission network planning solution decreases the investment and improves the renewable energy recommendation.

The outage of power system equipment is one of the most important factors that affect the reliability and economy of power system. It is crucial to consider the influence of contingencies elaborately in planning problem. In this paper, a distributionally robust transmission expansion planning model is proposed in which the uncertainty of contingency probability is considered. The uncertainty of contingency probability is described by uncertainty interval based on the outage rate of single equipment. An epigraph reformulation and Benders decomposition are applied to solve the proposed model. Finally, the feasibility and effectiveness of the proposed model are illustrated on the IEEE RTS system and the IEEE 118-bus system.

A distributed active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for regional AC transmission system (TS) with wind farms (WFs). The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator (TSO), and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system. The optimal power flow (OPF) of the TS is relaxed by using the semidefinite programming (SDP) relaxation while the branch flow model is used to model the WF collection system. In the DARPC strategy, the large-scale strongly-coupled optimization problem is decomposed by using the ADMM, which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality. The boundary information is exchanged between the regional TS controller and WF controllers. Compared with the conventional OPF method of the TS with WFs, the optimality and accuracy of the system operation can be improved. Moreover, the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller. The protection of the information privacy can be enhanced. A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines (WTs) is used to validate the proposed DARPC strategy.

This paper proposes a stochastic optimal operation method for active distribution networks to improve system resilience. The source–network–load uncertainties are handled with a set of typical scenarios, and the system post-contingency security level is quantitatively evaluated by security distance. The formulated problem determines the on-load tap-changer tap position, the distributed generator (DG) output power, the DG power factor angle, the demand side management participation rate and the switch status to achieve the economic operation solution. The off-the-shelf solver is deployed to solve the proposed mixed-integer nonlinear programming model. Numerical simulations studied on a modified 104-bus distribution network validate the effectiveness and high performance of the presented method. The solutions indicate that the security-aware operation scheme can simultaneously improve system economy and resilience compared with the existing operation model. Moreover, the operation solution accommodates more renewable DGs.

Abstract The existing load forecasting method based on the per‐unit curve static decoupling (PCSD) would easily lead to the deviation and translation of forecasting results. To tackle this challenge, a per‐unit curve rotated decoupling (PCRD) method is proposed for day‐ahead load forecasting with convolutional neural network and temporal convolutional network framework. The PCRD method decomposes the load into three parts: the rotated per‐unit load curve, the 0 AM load, and the daily average load. The shape feature of the load curve is extracted by CNN, the temporal features of the 0 AM load and daily average load are extracted by TCN. The rotation operation is to rotate the per‐unit load curve at the midpoint of the curve until the first load point is aligned to the same point, in order to improve the similarity of per‐unit load curves and to alleviate the deflection of forecasting results. The 0 AM load can verify the accuracy of the daily average load, which alleviates the translation of forecasting results. Several experimental results show that the proposed method has higher accuracy and stability than the existing PCSD method. After repeated experiments on multiple data sets, the generalization ability of the model is also verified.

With the development of carbon electricity, achieving a low-carbon economy has become a prevailing and inevitable trend. Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a low- carbon economy. In this paper, a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed. First, renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy. Second, a two-layer generation planning model considering carbon trading and carbon capture technology is established. Specifically, the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale, and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale. Finally, the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid, which demonstrate the effectiveness of the proposed model.

Abstract In order to concentrate the frequency spectrum of the output voltage and improve the quality of grid currents for the three‐level neutral point clamped inverter with the model predictive control, this paper proposes an optimal switching sequence model predictive control algorithm. Based on the increments of grid currents and the neutral‐point voltage, the predictive model of the inverter is established in αβ frame. Moreover, including grid currents and the neutral point voltage tracking, the cost function is designed and simplified by arranging the voltage vector sequence in a sampling cycle appropriately. Meanwhile, the calculation of the optimal dwell time for each voltage vector sequence is derived by Lagrange multiplier method, and its solving process is simplified to reduce online computations. Furthermore, according to different voltage vector sectors, a voltage vector sequence preselection principle is introduced in this paper, besides, considering redundant small vectors in a pair have opposite affections on the neutral point voltage, the amount of voltage vector sequence that needs to be verified is further cut down, for obtaining the optimal voltage vector sequence. Finally, the experimental results show that the proposed method can concentrate the output frequency spectrums, which maintains a good quality of grid currents with a small neutral‐point voltage fluctuation.

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

This study proposes a deep reinforcement learning (DRL) based approach to analyze the optimal power flow (OPF) of distribution networks (DNs) embedded with renewable energy and storage devices. First, the OPF of the DN is formulated as a stochastic nonlinear programming problem. Then, the multi-period nonlinear programming decision problem is formulated as a Markov decision process (MDP), which is composed of multiple single-time-step sub-problems. Subsequently, the state-of-the-art DRL algorithm, i.e., proximal policy optimization (PPO), is used to solve the MDP sequentially considering the impact on the future. Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN. The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results. The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones. Comparative results demonstrate the effectiveness of the proposed approach.

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

The utilization of renewable energy in sending-end power grids is increasing rapidly, which brings difficulties to voltage control. This paper proposes a coordinated voltage control strategy based on model predictive control (MPC) for the renewable energy power plants of wind and solar power connected to a weak sending-end power grid (WSPG). Wind turbine generators (WTGs), photovoltaic arrays (PVAs), and a static synchronous compensator are coordinated to maintain voltage within a feasible range during operation. This results in the full use of the reactive power capability of WTGs and PVAs. In addition, the impact of the active power outputs of WTGs and PVAs on voltage control are considered because of the high R/X ratio of a collector system. An analytical method is used for calculating sensitivity coefficients to improve computation efficiency. A renewable energy power plant with 80 WTGs and 20 PVAs connected to a WSPG is used to verify the proposed voltage control strategy. Case studies show that the coordinated voltage control strategy can achieve good voltage control performance, which improves the voltage quality of the entire power plant.

Given the current situation of large-scale energy storage system (ESS) access in distribution network, a practical distributed ESS location and capacity optimization model is proposed. Firstly, a weighted voltage sensitivity is proposed to select the grid-connected node set of ESS. On this basis, the distributed ESS location model is established, which aims at reducing voltage deviation and active power loss of the distribution network. Then, an ESS partition method based on the improved flame propagation model is proposed, and the partition results are obtained by constructing the flammability of nodes, the wind direction of flame propagation, speed of flame propagation and other indicators. Based on partition results, the capacity optimization model is established with the maximum annual net income of energy stored in the partition as the objective function. Finally, the improved IEEE-33 bus distribution network is used to demonstrate the effectiveness and feasibility of the proposed model.

With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), self-healing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the self-healing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, metaheuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.

Appropriate distributed economic dispatch (DED) strategies are of great importance to manage wide-area controllable generators in wide-area regional power systems. Compared with existing works related to ED, where dispatch algorithms are carried out by a centralized controller, a practical DED scheme is proposed in this paper to achieve the optimal dispatch by appropriately allocating the load to generation units while guaranteeing consensus among incremental costs. The ED problem is decoupled into several parallel sub-problems by the primal-dual principle to address the computational issue of satisfying power balance between the demand and the supply from the distributed regional power system. The feasibility test and an innovative mechanism for unit commitment are then designed to handle extreme operation situations, such as low load level and surplus generation. In the designed mechanism, the on/off status of units is determined in a fully distributed framework, which is solved using the piecewise approximation method and the discrete consensus algorithm. In the algorithm, the push-sum protocol is proposed to increase the system adaptation on the time-varying communication topology. Moreover, consensus gain functions are designed to ensure the performance of the proposed DED under communication noise. Case studies on a standard IEEE 30-bus system demonstrate the effectiveness of our proposed methodology.

Static traffic assignment aims to disclose the spatial distribution of vehicular flow over a transportation network subject to given traffic demands, and plays an essential role in transportation engineering. User-optimal pattern adheres the individual rationality of motorists, in which everyone chooses a route that minimizes his own travel cost, while considering congestion effects influenced by the aggregated movement of vehicles. User optimal traffic assignment, which is also known as the user equilibrium, entails solving an optimization problem with a strictly convex objective function and linear constraints. However, the performances of general-purpose solvers are quite disappointing. This paper proposes two highly efficient computation models for the user equilibrium problem. The first one exploits the second-order cone reformulation of a convex power function, resulting in a second-order cone program, and no approximation is incurred. The second one approximates the convex objective function using a piece-wise linear function, and comes down to a linear program. An adaptive path generation oracle is devised in order to circumvent path enumeration in problem setup. Case studies demonstrate that the proposed method can deal with large-scale transportation systems, and outperforms the most popular iterative algorithm in the literature.

The rapidly increasing penetration of electric vehicles in modern metropolises has been witnessed during the past decade, inspired by financial subsidies as well as public awareness of climate change and environment protection. Integrating charging facilities, especially high-power chargers in fast charging stations, into power distribution systems remarkably alters the traditional load flow pattern, and thus imposes great challenges on the operation of distribution network in which controllable resources are rare. On the other hand, provided with appropriate incentives, the energy storage capability of electric vehicle offers a unique opportunity to facilitate the integration of distributed wind and solar power generation into power distribution system. The above trends call for thorough investigation and research on the interdependence between transportation system and power distribution system. This paper conducts a comprehensive survey on this line of research. The basic models of transportation system and power distribution system are introduced, especially the user equilibrium model, which describes the vehicular flow on each road segment and is not familiar to the readers in power system community. The modelling of interdependence across the two systems is highlighted. Taking into account such interdependence, applications ranging from long-term planning to short-term operation are reviewed with emphasis on comparing the description of traffic-power interdependence. Finally, an outlook of prospective directions and key technologies in future research is summarized.

Abstract Investment for renewables has been growing rapidly since the beginning of the new century, and the momentum is expected to sustain in order to mitigate the impact of anthropogenic climate change. Transition towards higher renewable penetration in the power industry will not only confront technical challenges, but also face socio-economic obstacles. The connected between environment and energy systems are also tightened under elevated penetration of renewables. This paper will provide an overview of some important challenges related to technical, environmental and socio-economic aspects at elevated renewable penetration. An integrated analytical framework for interlinked technical, environmental and socio-economic systems will be presented at the end.

Abstract An electric vehicle (EV) centred ecosystem has not yet been formed, the existing limited statistic data are far from enough for the analysis of EV users’ travel and charge behaviors, which however tends to be affected by many certain and uncertain factors. An experimental economics (EE) based simulation method can be used to analyze the behaviors of key participants in a system. However, it is restricted by the system size, experimental site and the number of qualified human participants. Therefore, this method is hard to be adopted for the behavioral analysis of a large number of human participants. In this paper, a new method combining a questionnaire statistics and the EE-based simulation is proposed. The causal relationship is considered in the design of the questionnaires and data extraction, then a multi-agent modeling integration method is introduced in the EE-based simulation, which enables the integration of causal/statistical/behavioral models into the multi-agent framework to reflect the EV users’ travel willingness statistically. The generated multi-agents are used to replace human participants in the EE-based simulation in order to evaluate EV users’ travel demands in different scenarios, and compare the differences of simulated or measured travel behaviors between potential EV users and internal combustion engine (ICE) vehicle users.

The electronic power transformer (EPT) has higher power losses than the conventional transformer. However, the EPT can correct the power factor, compensate the unbalanced current, and reduce the line power losses in the distribution network. Therefore, the higher power losses of the EPT and the consequent reduced power losses in the distribution network require a comprehensive consideration when comparing the power losses of the EPT and conventional transformer. In this paper, a comprehensive power losses analysis model for the EPT in distribution networks is proposed. By analyzing the EPT self-losses and considering the impact of the nonunity power factor and the three-phase unbalanced current, the overall power losses in the distribution network when using the EPT to replace the conventional transformer is analyzed, and the conditions in which the application of the EPT can cause less power losses are obtained. Based on this, the sensitivity analysis for the EPT comprehensive power losses model is carried out by comparing the value of each parameter variation impact on the EPT losses model. In the case study, the validity of the comprehensive power losses model is verified.

When the simplex algorithm is used to calculate a linear programming (LP) problem, if the matrix is a sparse matrix, it will be possible to lead to many zero-length calculation steps, and even iterative cycle will appear. To deal with the problem, a new pivoting method is proposed in this paper. The principle of this method is to avoid choosing the row which the value of the element in the right side of constraint expression for LP in this row is zero as the row of the pivot element to make the matrix in LP density and ensure that most subsequent steps will improve the value of the objective function. One step following this principle is inserted in the existing LP algorithm to reselect the pivot element. Both the conditions for inserting this step and the maximum number of allowed insertion steps are determined. In the case study, taking several numbers of LP problems as examples, the results indicate that this method can effectively improve the efficiency of LP for the sparse matrix.

With the frequent occurrence of extreme weather events in the world, the ability of power system to resist disasters has attracted more attention. In this paper, a method to evaluate the resilience of distribution networks by focusing on the impact of critical loads under extreme weather events is proposed, and typhoon is taken as the representative to formulate a vulnerability curve of components. Furthermore, the Monte Carlo method is used to simulate the whole process of extreme weather disaster and to generate the fault scenario. Different weights are assigned to different levels of load according to the importance of load, and the weighted loss of load is selected as the evaluation index of resilience. Finally, the method is verified by the IEEE-33 bus system. The results demonstrate that this method is effective to quantify the resilience of distribution networks under extreme weather events.

This paper proposes an optimal coordinated method for electric vehicles (EVs) participating in frequency regulation (FR) under different power system operation states (PSOSs). In the proposed methodology, the FR power of EVs and generators is coordinated with different optimization objectives for power system secure and economic operations. When a power system operates in normal state, the minimum FR cost is used as an optimization objective considering the battery degradation cost. In the abnormal state, the minimum frequency restoring time is considered in the optimization objective. Based on the optimized results in different scenarios, the output power coordinated control rule between EVs and generators is drawn. Simulations on an interconnected two-area power system have validated the superiority of the proposed optimized coordinated control strategy.

Abstract As the power control technology of wind farms develops, the output power of wind farms can be constant, which makes it possible for wind farms to participate in power system restoration. However, due to the uncertainty of wind energy, the actual output power can’t reach a constant dispatch power in all time intervals, resulting in uncertain power sags which may induce the frequency of the system being restored to go outside the security limits. Therefore, it is necessary to optimize the dispatch of wind farms participating in power system restoration. Considering that the probability distribution function (PDF) of transient power sags is hard to obtain, a robust optimization model is proposed in this paper, which can maximize the output power of wind farms participating in power system restoration. Simulation results demonstrate that the security constraints of the restored system can be kept within security limits when wind farm dispatch is optimized by the proposed method.

Device-to-Device (D2D) communication is one of the critical technologies for the fifth-generation network, which allows devices to communicate directly with each other while increasing transmission rate, but this communication is vulnerable to interference. When video transmission is carried out in an environment with interference, problems such as high packet loss rate, poor quality of the video, and blurred screen may exist. These problems can be effectively solved by redundant coding operations at the source node, but the extra coding operation imposes a heavy computational burden on the source node. In order to alleviate the computational overhead of the source node, reduce transmission delay, and guarantee transmission quality, this paper proposes an efficient video multicast transmission scheme based on Random Linear Network Coding (RLNC) in D2D networks. In the scheme, the receiving devices in the transmission participate in the process of generating repair packets that are used to remedy the loss of encoded packets during transmission. The source node multicasts the encoded video file. The receiving nodes re-encode the received data packets with RLNC and then send them to the network again. The nearby nodes can decode the original data through the encoded or re-encoded data packets. The performance of the proposed scheme is evaluated through both simulation and real experiments. The experimental results show that compared with the traditional RLNC scheme, this scheme could balance the computation overhead of the mobile devices and reduce the encoding and decoding delay by about 8%. When the packet loss rate is high, the proposed scheme can obtain better video quality than the traditional replication-based scheme.

Based on the benchmark solid oxide fuel cell (SOFC) dynamic model for power system studies and the analysis of the SOFC operating conditions, the nonlinear programming (NLP) optimization method was used to determine the maximum electrical efficiency of the grid-connected SOFC subject to the constraints of fuel utilization factor, stack temperature and output active power. The optimal operating conditions of the grid-connected SOFC were obtained by solving the NLP problem considering the power consumed by the air compressor. With the optimal operating conditions of the SOFC for the maximum efficiency operation obtained at different active power output levels, a hierarchical load tracking control scheme for the grid-connected SOFC was proposed to realize the maximum electrical efficiency operation with the stack temperature bounded. The hierarchical control scheme consists of a fast active power control and a slower stack temperature control. The active power control was developed by using a decentralized control method. The efficiency of the proposed hierarchical control scheme was demonstrated by case studies using the benchmark SOFC dynamic model.

Microgrid is a good option to integrate renewable energy sources (RES) into power systems. In order to deal with the intermittent characteristics of the renewable energy based distributed generation (DG) units, a fuzzy-logic based coordinated control strategy of a battery energy storage system (BESS) and dispatchable DG units is proposed for the microgrid management system (MMS). In the proposed coordinated control strategy, the BESS is used to minimize active power exchange at the point of common coupling of the microgrid for grid-connected operation, and is used for frequency control for island operation. The efficiency of the proposed control strategy was tested by case studies using DIgSILENT/ PowerFactroy.

Traditional experimental economics methods often consume enormous resources of qualified human participants, and the inconsistence of a participant's decisions among repeated trials prevents investigation from sensitivity analyses. The problem can be solved if computer agents are capable of generating similar behaviors as the given participants in experiments. An experimental economics based analysis method is presented to extract deep information from questionnaire data and emulate any number of participants. Taking the customers' willingness to purchase electric vehicles (EVs) as an example, multi-layer correlation information is extracted from a limited number of questionnaires. Multi-agents mimicking the inquired potential customers are modelled through matching the probabilistic distributions of their willingness embedded in the questionnaires. The authenticity of both the model and the algorithm is validated by comparing the agent-based Monte Carlo simulation results with the questionnaire-based deduction results. With the aid of agent models, the effects of minority agents with specific preferences on the results are also discussed.