Abstract:Nuclear power development is a complex issue spanning cyber, physical, and social systems that is essential to achieving energy security and climate goals. With the ongoing worldwide trend towards carbon neutrality, the positioning of nuclear power in energy mix should be reconsidered. This paper aims to present a systematic review of current research on optimization of nuclear power development. The concept of cyber-physical-social system in energy (CPSSE) is adopted, which provides a suitable perspective and enables the review of relevant studies to achieve some novel insights. Based on the CPSSE, firstly, a research framework is established and the main research elements in optimization are identified, followed by a proposed conceptual risk-based optimization model. Secondly, current studies are analyzed and classified into four categories according to the research boundary. The status quo and limitations are discussed. It is found that the research results of nuclear-specific issues have not been well integrated into the optimization of nuclear power. As a relatively reliable power supply, nuclear power is capable of maintaining power and electricity adequacy of the whole system, especially in the case of power shortage caused by long-period low output of renewable energy or extreme external disasters. This superiority should not be ignored in the optimization. Other critical factors that should be further considered include disruptive technologies, nuclear safety, energy policies, and stakeholder behaviors. Finally, suggestions are given for future research.

Abstract:Security-constrained unit commitment (SCUC) has been extensively studied as a key decision-making tool to determine optimal power generation schedules in the operation of electricity market. With the development of emerging power grids, fruitful research results on SCUC have been obtained. Therefore, it is essential to review current work and propose future directions for SCUC to meet the needs of developing power systems. In this paper, the basic mathematical model of the standard SCUC is summarized, and the characteristics and application scopes of common solution algorithms are presented. Customized models focusing on diverse mathematical properties are then categorized and the corresponding solving methodologies are discussed. Finally, research trends in the field are prospected based on a summary of the state-of-the-art and latest studies. It is hoped that this paper can be a useful reference to support theoretical research and practical applications of SCUC in the future.

Abstract:Transmission network expansion can significantly improve the penetration level of renewable generation. However, existing studies have not explicitly revealed and quantified the trade-off between the investment cost and penetration level of renewable generation. This paper proposes a distributionally robust optimization model to minimize the cost of transmission network expansion under uncertainty and maximize the penetration level of renewable generation. The proposed model includes distributionally robust joint chance constraints, which maximize the minimum expectation of the renewable utilization probability among a set of certain probability distributions within an ambiguity set. The proposed formulation yields a two-stage robust optimization model with variable bounds of the uncertain sets, which is hard to solve. By applying the affine decision rule, second-order conic reformulation, and duality, we reformulate it into a single-stage standard robust optimization model and solve it efficiently via commercial solvers. Case studies are carried on the Garver 6-bus and IEEE 118-bus systems to illustrate the validity of the proposed method.

Abstract: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.

Abstract:A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed. To obtain the preferred models, this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data, and carries out a comprehensive evaluation. First, according to Dempster-Shafer (D-S) evidence theory, a new accuracy indicator based on the relative error (RE) is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time. Next, a new dimensionless method is proposed, which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator, to avoid the conflict between pieces of evidence caused by the minimum value of zero. On this basis, the evidence fusion is used to obtain the comprehensive evaluation value of each model. Then, the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described. Finally, the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Abstract:This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids’ success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NILM have not dealt with practical problems when applied to power grid, i.e., ① limited communication for slow-change systems; ② requirement of low-cost hardware at the users’ side; and ③ inconvenience to adapt to new households. Therefore, a novel NILM algorithm based on biology-inspired spiking neural network (SNN) has been developed to overcome the existing challenges. To provide intelligence in NILM, the developed SNN features an unsupervised learning rule, i.e., spike-time dependent plasticity (STDP), which only requires the user to label one instance for each appliance while adapting to a new household. To upgrade the feasibility in NILM, the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor (RC) circuit. In addition, a distributed computing system has been designed that divides the SNN into two parts, i.e., smart outlets and local servers. Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM, the high-frequency data can be easily compressed as the spike times, and are sent to the local server with limited communication capability, whereas it is unable to handle the traditional NILM. Finally, a series of experiments are conducted using a benchmark public dataset. Meanwhile, the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.

Abstract:Adding the auxiliary frequency control function to the wind turbine generator (WTG) is a solution to the frequency security problem of the power system caused by the replacement of the synchronous generator (SG) by the WTG. The auxiliary frequency control using rotor kinetic energy is an economical scheme because the WTG still runs at the maximum power point during normal operation. In this paper, the functional optimization model of the auxiliary frequency control strategy of WTG is established. The optimal auxiliary frequency control strategy is obtained by solving the model numerically. As for the practical realization of the control strategy, the coordination of the auxiliary frequency control with the maximum power point tracking (MPPT) control is studied. The practical auxiliary frequency control strategy is modified to adapt to different power disturbances in the system, and the parameter setting method is also proposed. The sensitivity of system frequency to control parameters is studied. Finally, the simulation results verify the effectiveness and practicability of the proposed control strategy.

Abstract:Battery energy storage systems (BESSs) are expected to play a crucial role in the operation and control of active distribution networks (ADNs). In this paper, a holistic state estimation framework is developed for ADNs with BESSs integrated. A dynamic equivalent model of BESS is developed, and the state transition and measurement equations are derived. Based on the equivalence between the correction stage of the iterated extended Kalman filter (IEKF) and the weighted least squares (WLS) regression, a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs, especially the state of charge (SOC). A bad data processing method is also presented. The simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs, providing comprehensive situational awareness for the whole system.

Abstract:Point of common coupling (PCC) arrays are the most prominent and widely-used intermittent distributed generations (DGs). Due to the right-of-way, environmental, economical and other restrictions, the connection of these types of DGs to the preferred point of the distribution network is very difficult or impossible in some cases. Therefore, because of non-optimal locations, they may cause a voltage rise at the PCC. In this paper, a coordinated design of switchable capacitor banks (SCBs) with dynamic reconfiguration of the distribution network is proposed to avoid low- and high-voltage violations. The distribution network reconfiguration is implemented to mitigate the voltage rise at PCCs and capacitor banks (CBs) to solve the low-voltage problem. A novel method is presented for determining the optimal size of CBs. The proposed capacitor sizing method (CSM) effectively determines the optimal values of reactive power for the given nodes. The optimal locations of SCB are determined using particle swarm optimization algorithm. The 24-hour reactive power curve optimized by the proposed method plays a pivotal role in designing SCBs. The simulation results show that the implementation of the dynamic network reconfiguration and the placement of SCB is required to maintain a standard voltage profile for better employment of DG embedded distribution networks.

Abstract:The emergence of dispersed generation, smart grids, and deregulated electricity markets has increased the focus on enhancing the performance of distribution systems. This paper proposes a method to reduce the energy loss and improve the reliability of distribution systems by performing distribution network reconfiguration (DNR) and distributed generator (DG) allocation. In this study, the intermittent nature of renewable-based DGs and the load profile are considered using a probabilistic method. The study investigates different annual plans based on the seasonal power profiles of DGs and the load to minimize the combined cost function of annual energy loss and annual energy not served. The proposed method is implemented using the firefly algorithm (FA), which is one of the meta-heuristic optimization algorithms. Several case studies are investigated using the IEEE 33-bus distribution system to highlight the effectiveness of the method.

Abstract:Congestions are becoming a significant issue with an increasing number of occurrences in distribution networks due to the growing penetration of distributed generation and the expected development of electric mobility. Fair congestion management (CM) policies and prices require proper indices of congested areas and contributions of customer to congestions. This paper presents spatial and temporal indices for rapidly recognizing the seriousness of congestions from the perspectives of both magnitude violation and duration to prioritize the affected areas where CM procedures should be primarily activated. Besides, indices are presented which describe the contributions of customers to the congestions. Simulation tests on IEEE 123-bus and Australian 23-bus low-voltage distribution test feeders illustrate the calculation and capabilities of the proposed indices in balanced and unbalanced systems.

Abstract:In this paper, inspired by the concept of virtual inertia in alternating current (AC) systems, a virtual impedance controller is proposed for the dynamic improvement of direct current microgrids (DCMGs). A simple and inexpensive method for injecting inertia into the system is used to adjust the output power of each distributed generation unit without using additional equipment. The proposed controller consists of two components: a virtual capacitor and a virtual inductor. These virtual components can change the rate of change of the DC bus voltage and also improve the transient response. A small-signal analysis is carried out to verify the impact of the proposed control strategy. Numerical simulation studies validate the effectiveness of the proposed solution for increasing the inertia of DCMGs.

Abstract:This paper proposes a novel distributed event-triggered secondary control method to overcome the drawbacks of primary control for direct current (DC) microgrids. With event-triggered distributed communication, the proposed control method can achieve system-wide control of parallel distrubted generators (DGs) with two main control objectives: ① estimate the average bus voltage and regulate it at the nominal value; ② achieve accurate current sharing among the DGs in proportion to their power output ratings. Furthermore, the proposed control strategy can be implemented in a distributed way that shares the required tasks among the DGs. Thus, it shows the advantages of being flexible and scalable. Furthermore, this paper proposes a simple event-triggered condition that does not need extra state estimator. Thus, limited communication among neighbors is required only when the event-triggered condition is satisfied, which significantly reduces the communication burden at the cyber layer.

Abstract:One battery energy storage system (BESS) can be used to provide different services, such as energy arbitrage (EA) and frequency regulation (FR) support, etc.， which have different revenues and lead to different battery degradation profiles. This paper proposes a whole-lifetime coordinated service strategy to maximize the total operation profit of BESS. A multi-stage battery aging model is developed to characterize the battery aging rates during the whole lifetime. Considering the uncertainty of electricity price in EA service and frequency deviation in FR service, the whole problem is formulated as a two-stage stochastic programming problem. At the first stage, the optimal service switching scheme between the EA and FR services are formulated to maximize the expected value of the whole-lifetime operation profit. At the second stage, the output power of BESS in EA service is optimized according to the electricity price in the hourly timescale, whereas the output power of BESS in FR service is directly determined according to the frequency deviation in the second timescale. The above optimization problem is then converted as a deterministic mixed-integer nonlinear programming (MINLP) model with bilinear items. McCormick envelopes and a bound tightening algorithm are used to solve it. Numerical simulation is carried out to validate the effectiveness and advantages of the proposed strategy.

Abstract:This paper presents a mixed-integer linear programming (MILP) formulation for sizing and siting of battery energy storage systems (BESSs). The problem formulation seeks to minimize both operation costs and BESS investment. The proposed model includes restrictions of the conventional security-constrained unit commitment problem, a piece-wise linear approximation to model power losses, and a linear model of hydro generation units. The proposed model is tested in a 6-bus test system and a 15-bus system representing the Colombian power system. For the two studied systems, simulation results show that the reduction of operation costs due to the installation of BESSs compensates the investments, under some of the considered technical cost cases. Additionally, results show that adequate sizing and siting of BESSs reduce renewable energy curtailment in the Colombian power system with high penetration of fluctuating renewable generation.

Abstract:The increased presence of electric vehicle charging locations in a power system with high penetration of intermittent wind power potentially leads to operation complexities resulting in abnormal impacts. This paper proposes an innovative framework for assessing the impact of plug-in electric vehicle (PEV) charging locations on a power system with integrated wind farms, incorporating dynamic thermal limits (DTLs). The framework comprises Monte Carlo simulation, which is embedded with stochastic modeling of various uncertainties under the key operating conditions. As part of the modeling framework, the transmission lines are ranked in accordance with the lowest level of expected energy not supplied. The PEV charging demand is then modeled by incorporating DTLs and applied to the least stressed transmission lines, following the IEEE 738-2006 standard. The new assessment framework is investigated using an IEEE 24-bus test system. The results demonstrate that applying DTLs on the least stressed transmission lines in conjunction with the integration of decentralized wind farms and strategic charging location of PEVs significantly improves the security of the energy supply and considerably reduces interruption costs, as opposed to not having such a framework.

Abstract:A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle (EV) owners. Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior, we propose a deep reinforcement learning based method for the minimization of individual EV charging cost. The charging problem is first formulated as a Markov decision process (MDP), which has unknown transition probability. A modified long short-term memory (LSTM) neural network is used as the representation layer to extract temporal features from the electricity price signal. The deep deterministic policy gradient (DDPG) algorithm, which has continuous action spaces, is used to solve the MDP. The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner. Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2% compared with other benchmark methods.

Abstract:Improving the flexibility of combined heat and power (CHP) units is an important way to solve the problem of wind power accommodation in northern China. Firstly, this paper analyzes the principle of an extraction-type CHP unit, calculates its safe operation range, and analyzes its contradiction between heating and peaking. Secondly, the safe operation ranges of the CHP unit with several flexibility modifications are further calculated, which involve two-stage bypass, low-pressure cylinder (LPC) removal, heat storage tank, and electric boiler. Finally, based on the safe operation ranges, their effects on improving the capabilities of deep peak shaving and wind power accommodation are compared, and their adaptabilities to different wind scenarios are analyzed. The results show that: ① all flexibility modifications can improve the deep peak shaving capability of the CHP unit, especially for the two-stage bypass and the electric boiler; ② LPC removal modification can accommodate wind power to some extent, but most of wind power is still abandoned; ③ heat storage tank modification is unstable in different wind scenarios, which is determined by the surplus heating capability during the daytime.

Abstract:This paper develops deep reinforcement learning (DRL) algorithms for optimizing the operation of home energy system which consists of photovoltaic (PV) panels, battery energy storage system, and household appliances. Model-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV generation. However, discrete-continuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous actions. Thus, a mixed deep reinforcement learning (MDRL) algorithm is proposed, which integrates deep Q-learning (DQL) algorithm and deep deterministic policy gradient (DDPG) algorithm. The DQL algorithm deals with discrete actions, while the DDPG algorithm handles continuous actions. The MDRL algorithm learns optimal strategy by trial-and-error interactions with the environment. However, unsafe actions, which violate system constraints, can give rise to great cost. To handle such problem, a safe-MDRL algorithm is further proposed. Simulation studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy management. The proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test dataset. Moreover, the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

Abstract:In recent years, rumors have been shown to have a significant impact on individual and societal activities. As renewables play an increasingly significant role in electricity markets, certain rumors may deviate the bidding behavior of market entities and eventually affect the performance of market operations. In this study, we attempt to reveal the general threats caused by rumors in the context of day-ahead electricity markets considering the integration of volatile renewables. First, we model the propagation of rumors in the societal system considering the weight of propagation resistance, which principally reflects the communication accessibility of market entities. Second, we develop an integrated two-layer network model to uncover the inherent coupling mechanism between market operations and rumor propagation. In particular, the role of electricity market operations on rumor propagation is characterized by changes in the truthfulness of rumors associated with electricity prices. The rumors, in turn, affect the bidding quantities of market entities in electricity market operations. Finally, numerical experiments are conducted on modified IEEE 6-bus and 118-bus systems. The results demonstrate the potential threats of rumors to electricity market operations with different penetration levels of renewables.

Abstract:This paper proposes a stochastic optimization model for generating the optimal price-maker trading strategy for a wind power producer using virtual bidding, which is a kind of financial tool available in most electricity markets of the United States. In the proposed model, virtual bidding is used to improve the wind power producer’s market power in the day-ahead (DA) market by trading at multiple buses, which are not limited to the locations of the wind units. The optimal joint wind power and virtual trading strategy is generated by solving a bi-level nonlinear stochastic optimization model. The upper-level problem maximizes the total expected profit of the wind power and virtual bidding while using the conditional value at risk (CVaR) for risk management. The lower-level problem represents the clearing process of the DA market. By using the Karush-Kuhn-Tucker (KKT) conditions, duality theory, and big-M method, the bi-level nonlinear stochastic model is firstly transferred into an equivalent single-level stochastic mathematical program with the equilibrium constraints (MPEC) model and then a mixed-integer linear programming (MILP) model, which can be solved by existing commercial solvers. To reduce the computational cost of solving the proposed stochastic optimization model for large systems, a method of reducing the number of buses considered for virtual bidding is proposed to simplify the stochastic MPEC model by reducing its decision variables and constraints related to virtual bidding. Case studies are performed to show the effectiveness of the proposed model and the method of reducing the number of buses considered for virtual bidding. The impacts of the transmission limits, wind unit location, risk aversion parameters, wind power volatility, and wind and virtual capacities on the price-maker trading strategy are also studied through case studies.

Abstract:The mitigation of commutation failure (CF) depends on the accuracy of CF prediction. In terms of the large error of the existing extinction angle (EA) calculation during the fault transient period, a method for CF prediction and mitigation is proposed. Variations in both DC current and overlap angle (OA) are considered in the proposed method to predict the EA rapidly. In addition, variations in critical EA and the effect of firing angle (FA) on both DC current and OA are considered in the proposed method to obtain the accurate FA order for the control system. The proposed method can achieve good performance in terms of CF mitigation and reduce reactive consumption at the inverter side when a fault occurs. Simulation results based on the PSCAD/EMTDC show that the proposed method predicts CF rapidly and exhibits good performance in terms of CF mitigation.

Abstract:This paper proposes a system identification framework based on eigensystem realization to accurately model power electronic converters. The proposed framework affords an energy-based optimal reduction method to precisely identify the dynamics of power electronic converters from simulated or actual raw data measured at the converter’s ports. This method does not require any prior knowledge of the topology or internal parameters of the converter to derive the system modal information. The accuracy and feasibility of the proposed method are exhaustively evaluated via simulations and practical tests on a software-simulated and hardware-implemented dual active bridge (DAB) converter under steady-state and transient conditions. After various comparisons with the Fourier series-based generalized average model, switching model, and experimental measurements, the proposed method attains a root mean square error (RMSE) of less than 1% with respect to the actual raw data. Moreover, the computational effort is reduced to 1/8.6 of the Fourier series-based model.

Abstract:Power flow (PF) is one of the most important calculations in power systems. The widely-used PF methods are the Newton-Raphson PF (NRPF) method and the fast-decoupled PF (FDPF) method. In smart grids, power generations and loads become intermittent and much more uncertain, and the topology also changes more frequently, which may result in significant state shifts and further make NRPF or FDPF difficult to converge. To address this problem, we propose a data-driven PF (DDPF) method based on historical/simulated data that includes an offline learning stage and an online computing stage. In the offline learning stage, a learning model is constructed based on the proposed exact linear regression equations, and then the proposed learning model is solved by the ridge regression (RR) method to suppress the effect of data collinearity. In online computing stage, the nonlinear iterative calculation is not needed. Simulation results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.