Abstract:

Abstract:Industrial, commercial, and residential facilities are progressively adopting automation and generation capabilities. By having flexible demand and renewable energy generation, traditional passive customers are becoming active participants in electric power system operations. Through profound coordination among grid operators and active customers, the facilities’ capability for demand response (DR) and distributed energy resource (DER) management will be valuable asset for ancillary services (ASs). To comply with the increasing demand and flexible energy, utilities urgently require standards, regulations, and programs to efficiently handle load-side resources without trading off stability and reliability. This study reviews different types of customers’ flexibilities for DR, highlighting their capabilities and limitations in performing local ancillary services (LASs), which should benefit the power grid by profiting from it through incentive mechanisms. Different financial incentives and techniques employed around the world are presented and discussed. The potential barriers in technical and regulatory aspects are successfully identified and potential solutions along with future guidance are discussed.

Abstract:The load demand and distributed generation (DG) integration capacity in distribution networks (DNs) increase constantly, and it means that the violation of security constraints may occur in the future. This can be further worsened by short-term power fluctuations. In this paper, a scheduling method based on a multi-objective chance-constrained information-gap decision (IGD) model is proposed to obtain the active management schemes for distribution system operators (DSOs) to address these problems. The maximum robust adaptability of multiple uncertainties, including the deviations of growth prediction and their relevant power fluctuations, can be obtained based on the limited budget of active management. The systematic solution of the proposed model is developed. The max term constraint in the IGD model is converted into a group of normal constraints corresponding to extreme points of the max term. Considering the stochastic characteristics and correlations of power fluctuations, the original model is equivalently reformulated by using the properties of multivariate Gaussian distribution. The effectiveness of the proposed model is verified by a modified IEEE 33-bus distribution network. The simulation result delineates a robust accommodation space to represent the adaptability of multiple uncertainties, which corresponds to an optional active management strategy set for future selection.

Abstract:Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems. However, traditional intelligent methods limit the use of the physical structures and data information of power networks. To this end, this study proposes a fault diagnostic model for distribution systems based on deep graph learning. This model considers the physical structure of the power network as a significant constraint during model training, which endows the model with stronger information perception to resist abnormal data input and unknown application conditions. In addition, a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability. This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults. In addition, a multi-task learning framework is constructed for fault location and fault type analysis, which improves the performance and generalization ability of the model. The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model. Finally, different fault conditions, topological changes, and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model. Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

Abstract:The increasing flexibility of active distribution systems (ADSs) coupled with the high penetration of renewable distributed generators (RDGs) leads to the increase of the complexity. It is of practical significance to achieve the largest amount of RDG penetration in ADSs and maintain the optimal operation. This study establishes an alternating current (AC)/direct current (DC) hybrid ADS model that considers the dynamic thermal rating, soft open point, and distribution network reconfiguration (DNR). Moreover, it transforms the optimal dispatching into a second-order cone programming problem. Considering the different control time scales of dispatchable resources, the following two-stage dispatching framework is proposed. ① The day-ahead dispatch uses hourly input data with the goal of minimizing the grid loss and RDG dropout. It obtains the optimal 24-hour schedule to determine the dispatching plans for DNR and the energy storage system. ② The intraday dispatch uses 15 min of input data for 1-hour rolling-plan dispatch but only executes the first 15 min of dispatching. To eliminate error between the actual operation and dispatching plan, the first 15 min is divided into three 5-min step-by-step executions. The goal of each step is to trace the tie-line power of the intraday rolling-plan dispatch to the greatest extent at the minimum cost. The measured data are used as feedback input for the rolling-plan dispatch after each step is executed. A case study shows that the comprehensive cooperative ADS model can release the line capacity, reduce losses, and improve the penetration rate of RDGs. Further, the two-stage dispatching framework can handle source-load fluctuations and enhance system stability.

Abstract:Due to the lack of flexible interconnection devices, power imbalances between networks cannot be relieved effectively. Meanwhile, increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences. To improve the operational economy lowered by spatiotemporal power imbalances, this paper proposes a two-stage optimization strategy for active distribution networks (ADNs) interconnected by soft open points (SOPs). The SOPs and energy storage system (ESS) are adopted to transfer power spatially and temporally, respectively. In the day-ahead scheduling stage, massive stochastic scenarios against the uncertainty of wind turbine output are generated first. To improve computational efficiency in massive stochastic scenarios, an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established. The introduction of sensitivities prevents violations of voltage and current. Then, the operating ranges (ORs) of the active power of SOPs and the state of charge (SOC) of ESS are obtained from models between networks and within the networks, respectively. In the intraday corrective control stage, based on day-ahead ORs, a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour. Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.

Abstract:The increased deployment of electricity-based hydrogen production strengthens the coupling of power distribution system (PDS) and hydrogen energy system (HES). Considering that power to hydrogen (PtH) has great potential to facilitate the usage of renewable energy sources (RESs), the coordination of PDS and HES is important for planning purposes under high RES penetration. To this end, this paper proposes a multi-stage co-planning model for the PDS and HES. For the PDS, investment decisions on network assets and RES are optimized to supply the growing electric load and PtHs. For the HES, capacities of PtHs and hydrogen storages (HSs) are optimally determined to satisfy hydrogen load considering the hydrogen production, tube trailer transportation, and storage constraints. The overall planning problem is formulated as a multi-stage stochastic optimization model, in which the investment decisions are sequentially made as the uncertainties of electric and hydrogen load growth states are revealed gradually over periods. Case studies validate that the proposed co-planning model can reduce the total planning cost, promote RES consumption, and obtain more flexible decisions under long-term load growth uncertainties.

Abstract:Most existing distribution networks are difficult to withstand the impact of meteorological disasters. With the development of active distribution networks (ADNs), more and more upgrading and updating resources are applied to enhance the resilience of ADNs. A two-stage stochastic mixed-integer programming (SMIP) model is proposed in this paper to minimize the upgrading and operation cost of ADNs by considering random scenarios referring to different operation scenarios of ADNs caused by disastrous weather events. In the first stage, the planning decision is formulated according to the measures of hardening existing distribution lines, upgrading automatic switches, and deploying energy storage resources. The second stage is to evaluate the operation cost of ADNs by considering the cost of load shedding due to disastrous weather and optimal deployment of energy storage systems (ESSs) under normal weather condition. A novel modeling method is proposed to address the uncertainty of the operation state of distribution lines according to the canonical representation of logical constraints. The progressive hedging algorithm (PHA) is adopted to solve the SMIP model. The IEEE 33-node test system is employed to verify the feasibility and effectiveness of the proposed method. The results show that the proposed model can enhance the resilience of the ADN while ensuring economy.

Abstract:With the large-scale distributed generations (DGs) being connected to distribution network (DN), the traditional day-ahead reconfiguration methods based on physical models are challenged to maintain the robustness and avoid voltage off-limits. To address these problems, this paper develops a deep reinforcement learning method for the sequential reconfiguration with soft open points (SOPs) based on real-time data. A state-based decision model is first proposed by constructing a Marko decision process-based reconfiguration and SOP joint optimization model so that the decisions can be achieved in milliseconds. Then, a deep reinforcement learning joint framework including branching double deep Q network (BDDQN) and multi-policy soft actor-critic (MPSAC) is proposed, which has significantly improved the learning efficiency of the decision model in multi-dimensional mixed-integer action space. And the influence of DG and load uncertainty on control results has been minimized by using the real-time status of the DN to make control decisions. The numerical simulations on the IEEE 34-bus and 123-bus systems demonstrate that the proposed method can effectively reduce the operation cost and solve the overvoltage problem caused by high ratio of photovoltaic (PV) integration.

Abstract:This paper proposes a stochastic programming (SP) method for coordinated operation of distributed energy resources (DERs) in the unbalanced active distribution network (ADN) with diverse correlated uncertainties. First, the three-phase branch flow is modeled to characterize the unbalanced nature of the ADN, schedule DER for three phases, and derive a realistic DER allocation. Then, both active and reactive power resources are co-optimized for voltage regulation and power loss reduction. Second, the battery degradation is considered to model the aging cost for each charging or discharging event, leading to a more realistic cost estimation. Further, copula-based uncertainty modeling is applied to capture the correlations between renewable generation and power loads, and the two-stage SP method is then used to get final solutions. Finally, numerical case studies are conducted on an IEEE 34- bus three-phase ADN, verifying that the proposed method can effectively reduce the system cost and co-optimize the active and reactive power.

Abstract:High penetration of distributed renewable energy sources and electric vehicles (EVs) makes future active distribution network (ADN) highly variable. These characteristics put great challenges to traditional voltage control methods. Voltage control based on the deep Q-network (DQN) algorithm offers a potential solution to this problem because it possesses human-level control performance. However, the traditional DQN methods may produce overestimation of action reward values, resulting in degradation of obtained solutions. In this paper, an intelligent voltage control method based on averaged weighted double deep Q-network (AWDDQN) algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network (DDQN) algorithm. Using the proposed method, the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process (MDP) model which is solved by the AWDDQN algorithm. The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN. The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs，and compared with the DQN and DDQN algorithms based methods, and traditional mixed-integer nonlinear program based methods. The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

Abstract:The emergence of prosumers in distribution systems has enabled competitive electricity markets to transition from traditional hierarchical structures to more decentralized models such as peer-to-peer (P2P) and community-based (CB) energy transaction markets. However, the network usage charge (NUC) that prosumers pay to the electric power utility for network services is not adjusted to suit these energy transactions, which causes a reduction in revenue streams of the utility. In this study, we propose an NUC calculation method for P2P and CB transactions to address holistically economic and technical issues in transactive energy markets and distribution system operations, respectively. Based on the Nash bargaining (NB) theory, we formulate an NB problem for P2P and CB transactions to solve the conflicts of interest among prosumers, where the problem is further decomposed into two convex subproblems of social welfare maximization and payment bargaining. We then build the NUC calculation model by coupling the NB model and AC optimal power flow model. We also employ the Shapley value to allocate the NUC to consumers fairly for the NUC model of CB transactions. Finally, numerical studies on IEEE 15-bus and 123-bus distribution systems demonstrate the effectiveness of the proposed NUC calculation method for P2P and CB transactions.

Abstract:Direct current (DC) bus voltage stability is essential for the stable and reliable operation of a DC system. If an oscillation source can be quickly and accurately localized, the oscillation can be adequately eliminated. We propose a method based on the power spectral density for identifying the voltage oscillation source. Specifically, a DC distribution network model combined with the component connection method is developed, and the network is separated into multiple power modules. Compared with a conventional method, the proposed method does not require determining the model parameters of the entire power grid, which is typically challenging. Furthermore, combined with a novel judgment index, the oscillation source can be identified more intuitively and clearly to enhance the applicability to real power grids. The performance of the proposed method has been evaluated using the MATLAB/Simulink software and PLECS RT Box experimental platform. The simulation and experimental results verify that the proposed method can accurately identify oscillation sources in a DC distribution network.

Abstract:Nowadays, grid-connected renewable energy resources have widespread applications in the electricity market. However, providing household consumers with photovoltaic (PV) systems requires bilateral interfaces to exchange energy and data. In addition, residential consumers’ contribution requires guaranteed privacy and secured data exchange. Day-ahead dynamic pricing is one of the incentive-based demand response methods that has substantial effects on the integration of renewable energy resources with smart grids and social welfare. Different metering mechanisms of renewable energy resources such as feed-in tariffs, net metering, and net purchase and sale are important issues in power grid operation planning. In this paper, optimal condition decomposition method is used for day-ahead dynamic pricing of grid-connected residential renewable energy resources under different metering mechanisms: feed-in-tariffs, net metering, and net purchase and sale in conjunction with carbon emission taxes. According to the stochastic nature of consumers’ load and PV system products, uncertainties are considered in a two-stage decision-making process. The results demonstrate that the net metering with the satisfaction average of 68% for consumers and 32% for the investigated electric company leads to 28% total load reduction. For the case of net purchase and sale mechanism, a satisfaction average of 15% for consumers and 85% for the electric company results in 11% total load reduction. In feed-in-tariff mechanism, in spite of increased social welfare, load reduction does not take place.

Abstract:Owing to potential regulation capacities from flexible resources in energy coupling, storage, and consumption links, central energy stations (CESs) can provide additional support to power distribution network (PDN) in case of power disruption. However, existing research has not explicitly revealed the emergency response of PDN with leveraging multiple CESs. This paper proposes a decentralized self-healing strategy of PDN to minimize the entire load loss, in which multi-area CESs’ potentials including thermal storage and building thermal inertia, as well as the flexible topology of PDN, are reasonably exploited for service recovery. For sake of privacy preservation, the co-optimization of PDN and CESs is realized in a decentralized manner using adaptive alternating direction method of multipliers (ADMM). Furtherly, bilateral risk management with conditional value-at-risk (CVaR) for PDN and risk constraints for CESs is integrated to deal with uncertainties from outage duration. Case studies are conducted on a modified IEEE 33-bus PDN with multiple CESs. Numerical results illustrate that the proposed strategy can fully utilize the potentials of multi-area CESs for coordinated load restoration. The effectiveness of the performance and behaviors’ adaptation against random risks is also validated.

Abstract:A photovoltaic (PV)-rich low-voltage (LV) distribution network poses a limit on the export power of PVs due to the voltage magnitude constraints. By defining a customer export limit, switching off the PV inverters can be avoided, and thus reducing power curtailment. Based on this, this paper proposes a mixed-integer nonlinear programming (MINLP) model to define such optimal customer export. The MINLP model aims to minimize the total PV power curtailment while considering the technical operation of the distribution network. First, a nonlinear mathematical formulation is presented. Then, a new set of linearizations approximating the Euclidean norm is introduced to turn the MINLP model into an MILP formulation that can be solved with reasonable computational effort. An extension to consider multiple stochastic scenarios is also presented. The proposed model has been tested in a real LV distribution network using smart meter measurements and irradiance profiles from a case study in the Netherlands. To assess the quality of the solution provided by the proposed MILP model, Monte Carlo simulations are executed in OpenDSS, while an error assessment between the original MINLP and the approximated MILP model has been conducted.

Abstract:Scalable coordination of photovoltaic (PV) inverters, considering the uncertainty in PV and load in distribution networks (DNs), is challenging due to the lack of real-time communications. Decentralized PV inverter setpoints can be achieved to address this issue by capitalizing on the abundance of data from smart utility meters and the scalable architecture of artificial neural networks (ANNs). To this end, we first use an offline, centralized data-driven conservative convex approximation of chance-constrained optimal power flow (CVaR-OPF) in which conditional value-at-risk (CVaR) is used to compute reactive power setpoints of PV inverter, taking into account PV and load uncertainties in DNs. Following that, an artificial neural network (ANN) controller is trained for each PV inverter to emulate the optimal behavior of the centralized control setpoints of PV inverter in a decentralized fashion. Additionally, the voltage regulation performance of the developed ANN controllers is compared with other decentralized designs (local controllers) developed using model-based learning (regression-based controller), optimization (affine feedback controller), and case-based learning (mapping) approaches. Numerical tests using real-world feeders corroborate the effectiveness of ANN controllers in voltage regulation and loss minimization.

Abstract:The purpose of active distribution networks (ADNs) is to provide effective control approaches for enhancing the operation of distribution networks (DNs) and greater accommodation of distributed generation (DG) sources. With the integration of DG sources into DNs, several operational problems have drawn attention such as overvoltage and power flow alteration issues. These problems can be dealt with by utilizing distribution network reconfiguration (DNR) and soft open points (SOPs). An SOP is a power electronic device capable of accurately controlling active and reactive power flows. Another significant aspect often overlooked is the coordination of protection devices needed to keep the network safe from damage. When implementing DNR and SOPs in real DNs, protection constraints must be considered. This paper presents an ADN reconfiguration approach that includes DG sources, SOPs, and protection devices. This approach selects the ideal configuration, DG output, and SOP placement and control by employing particle swarm optimization (PSO) to minimize power loss while ensuring the correct operation of protection devices under normal and fault conditions. The proposed approach explicitly formulates constraints on network operation, protection coordination, DG size, and SOP size. Finally, the proposed approach is evaluated using the standard IEEE 33-bus and IEEE 69-bus networks to demonstrate the validity.

Abstract:The accessible and convenient hydrogen supply is the foundation of successful materialization for hydrogen-powered vehicles (HVs). This paper proposes a novel optimal scheduling model for gaseous-liquid hydrogen generation and storage plants powered by renewable energy to enhance the economic feasibility of investment. The gaseous-liquid hydrogen generation and storage plant can be regarded as an energy hub to supply concurrent service to both the transportation sector and ancillary market. In the proposed model, the power to multi-state hydrogen (P2MH) process is analyzed in detail to model the branched hydrogen flow constraints and the corresponding energy conversion relationship during hydrogen generation, processing, and storage. To model the coupling and interaction of diverse modules in the system, the multi-energy coupling matrix is developed, which can exhibit the mapping of power from the input to the output. Based on this, a multi-product optimal scheduling (MPOS) algorithm considering complementarity of different hydrogen products is further formulated to optimize dispatch factors of the energy hub system to maximize the profit within limited resources. The demand response signals are incorporated in the algorithm to further enhance the operation revenue and the scenario-based method is deployed to consider the uncertainty. The proposed methodology has been fully tested and the results demonstrate that the proposed MPOS can lead to a higher rate of return for the gaseous-liquid hydrogen generation and storage plant.

Abstract:In this study, a machine learning based method is proposed for creating synthetic eventful phasor measurement unit (PMU) data under time-varying load conditions. The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations (ODEs) to capture the transient behaviors of the system during voltage oscillation events. A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations. The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.

Abstract:Synchrophasor measurement units (PMUs) provide synchronized measurement data for wide-area applications. To improve the effectiveness of synchrophasor-based applications, field PMUs must be tested to ensure their performance and data quality. In the companion paper (Part I), we proposed a field PMU test and calibration framework consisting of a PMU calibrator and analysis center. Part I presents the development and test of the PMU calibrator. This paper focuses on the analysis center and field test applications. First, the critical component of the analysis center is the signal identification module, for which the step and oscillation signal identification methods are proposed. Here, the performance evaluation criteria of PMU in these two cases are different from others. The methods include a step signal detection method based on singular value decomposition (SVD), which has the capability of weak step detection to account for energy leakage of the signal during the step process, and an oscillation signal identification method based on SVD and fast Fourier transform, which can accurately extract oscillation components that benefit from the adaptive threshold setting method. Second, the analysis center software is implemented based on identification results. By integrating the PMU calibrator in Part I with the analysis center in Part II, we can examine in depth the field PMU test applications in three test scenarios, including standard, playback, and field signal test. Results demonstrate the effectiveness and applicability of the proposed field PMU test methods from both Parts I and II.

Abstract:This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment (TCUC). Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are popular approaches to solving TCUC. However, with many binary unit commitment variables, LR suffers from slow convergence and MILP presents heavy computation burden. The proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems. A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible (ASF) approaches, which is then leveraged to efficiently solve new TCUC instances online. On/off statuses of considerable units can be fixed in the online calculation according to the database, which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances. A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables, which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure changes. Numerical tests illustrate the efficiency of the proposed approach.

Abstract:As synchronous generators (SGs) are gradually displaced by renewable energy sources (RESs), the frequency stability of power systems deteriorates because RESs, represented by utility-scale solar and wind power sources, do not provide the inertial response, primary frequency response, secondary frequency response, and tertiary frequency regulation. As a result, the remaining SGs may not be sufficient to maintain the power balance and frequency stability. The concept and control strategies of virtual synchronous generators (VSGs) enable the inverter-based wind and solar power sources to emulate the outer characteristics of traditional SGs and participate in the active power and frequency control of power systems. This paper focuses on the automatic generation control (AGC) with virtual synchronous renewables (VSRs). First, the VSR strategy that enables the RESs to participate in AGC is introduced. Second, based on the interval representation of uncertainty, the output of RES is transformed into two portions, i.e., the dispatchable portion and the stochastic portion. In the dispatchable portion, the RESs can participate in AGC jointly with SGs. Accordingly, a security-constrained economic dispatch (SCED) model is built considering the RESs operating in VSR mode. Third, the solution strategy that employs the slack variables to acquire deterministic constraints is introduced. Finally, the proposed SCED model is solved based on the 6-bus and 39-bus systems. The results show that, compared with the maximum power point tracking (MPPT) mode, VSRs can participate in the active power and frequency control jointly with SGs, increase the maximum penetration level of RESs, and decrease the operating cost.

Abstract: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:This study utilizes hot dry rock (HDR) geothermal energy, which is not affected by climate, to address the capacity allocation of photovoltaic (PV)-storage hybrid power systems (HPSs) in frigid plateau regions. The study replaces the conventional electrochemical energy storage system with a stable HDR plant assisted by a flexible thermal storage (TS) plant. An HPS consisting of an HDR plant, a TS plant, and a PV plant is proposed. Game approaches are introduced to establish the game pattern model of the proposed HPS as the players. The annualized income of each player is used as the payoff function. Furthermore, non-cooperative game and cooperative game approaches for capacity allocation are proposed according to the interests of each player in the proposed HPS. Finally, the proposed model and approaches are validated by performing calculations for an HPS in the Gonghe Basin, Qinghai, China as a case study. The results show that in the proposed non-cooperative game approach, the players focus only on the individual payoff and neglect the overall system optimality. The proposed cooperative game approach for capacity allocation improves the flexibility of the HPS as well as the payoff of each game player. Thereby, the HPS can better satisfy the power fluctuation rate requirements of the grid and increase the equivalent firm capacity (EFC) of PV plants, which in turn indirectly guarantees the reliability of grid operation.

Abstract:Fault restoration techniques have always been crucial for distribution system operators (DSOs). In the last decade, it started to gain more and more importance due to the introduction of output-based regulations where DSO performances are evaluated according to frequency and duration of energy supply interruptions. The paper presents a tabu-search-based algorithm able to assist distribution network operational engineers in identifying solutions to restore the energy supply after permanent faults. According to the network property, two objective functions are considered to optimize either reliability or resiliency. The mathematical formulation includes the traditional feeders, number of switching operation limit, and radiality constraints. Thanks to the DSO of Milan, Unareti, the proposed algorithm has been tested on a real distribution network to investigate its effectiveness.

Abstract:In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems, the capability of distributed generators (DGs) to regulate the reactive power, the operation costs of the regulation equipment, and the current of the shunt capacitor of the cables are not considered. In this paper, a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed. This strategy considers the time-of-use price, the capability of the DGs to regulate the active and reactive power, the action costs of the regulation equipment, and the current of the shunt capacitors of the cables in a radial distribution system. Furthermore, the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency. To solve the second-stage model, only the model of each period needs to be solved. Then, their objective values are accumulated, revealing that the computation rate using the proposed method is much higher than that of existing methods. The effectiveness of the proposed method is validated by actual 4-bus, IEEE 33-bus, and PG 69-bus distribution systems.

Abstract:Photovoltaic (PV) power generation has highly penetrated in distribution networks, providing clean and sustainable energy. However, its uncertain and intermittent power outputs significantly impair network operation, leading to unexpected power loss and voltage fluctuation. To address the uncertainties, this paper proposes a multi-timescale affinely adjustable robust reactive power dispatch (MTAAR-RPD) method to reduce the network power losses as well as alleviate voltage deviations and fluctuations. The MTAAR-RPD aims to coordinate on-load tap changers (OLTCs), capacitor banks (CBs), and PV inverters through a three-stage structure which covers multiple timescales of “hour-minute-second”. The first stage schedules CBs and OLTCs hourly while the second stage dispatches the base reactive power outputs of PV inverter every 15 min. The third stage affinely adjusts the inverter reactive power output based on an optimized Q-P droop controller in real time. The three stages are coordinately optimized by an affinely adjustable robust optimization method. A solution algorithm based on a cutting plane algorithm is developed to solve the optimization problem effectively. The proposed method is verified through theoretical analysis and numerical simulations.

Abstract:Direct current (DC) microgrid consists of many parallel power converters that share load currents through the inductance of DC/DC converters. Usually, the inductance parameters are dependent on the physical implementation of the system, and their values may not match their nameplates. Such disparities could lead to unequal response characteristics of the system, which can potentially reduce the performances of the DC microgrid operation. This paper proposes a robust control strategy for inductive parametric uncertainties of DC/DC converters using an optimal control method with integral action. To achieve such a goal, the system model parameters with nominal values are transformed into parametric unmatched uncertainties to form a robust control problem, which is then transformed into a linear quadratic regulator problem. The inductance uncertainties are stabilized with the uncertainty dynamic algebraic Riccati equation (UDARE) using state feedback gain under linear quadratic regulator. The closed-loop control with integral action is adopted to achieve a steady-state error of zero on the DC-link voltage at any uncertainty of the inductive parameter, which subsequently ensures the equal load current sharing. Off-line simulations and real-time validations based on OpalRT have been conducted to demonstrate the effectiveness and robustness of the proposed robust control strategy.

Abstract:With the continuous development of information technology, data centers (DCs) consume significant and ever-growing amounts of electrical energy. Renewable energy sources (RESs) can act as clean solutions to meet this requirement without polluting the environment. Each DC serves numerous users for their data service demands, which are regarded as flexible loads. In this paper, the willingness to pay and time sensitivities of DC users are firstly explored, and the user-side demand response is then devised to improve the overall benefits of DC operation. Then, a Stackelberg game between a DC and its users is proposed. The upper-level model aims to maximize the profit of the DC, in which the time-varying pricing of data services is optimized, and the lower-level model addresses user ’s optimal decisions for using data services while balancing their time and cost requirements. The original bi-level optimization problem is then transformed into a single-level problem using the Karush-Kuhn-Tucker optimality conditions and strong duality theory, which enables the problem to be solved efficiently. Finally, case studies are conducted to demonstrate the feasibility and effectiveness of the proposed method, as well as the effects of the time-varying data service price mechanism on the RES accommodation.

Abstract:The growing number of renewable energy replacing conventional generators results in a loss of the reserve for frequency control in power systems, while many industrial power grids often have excess energy supply due to abundant wind and solar energy resources. This paper proposes a secondary frequency control (SFC) strategy that allows industrial power grids to provide emergency high-voltage direct current (HVDC) power support (EDCPS) for emergency to a system requiring power support through a voltage source converter (VSC) HVDC link. An architecture including multiple model predictive control (MPC) controllers with periodic communication is designed to simultaneously obtain optimized EDCPS capacity and minimize adverse effects on the providing power support (PPS) system. Moreover, a model of a virtual power plant (VPP) containing aluminum smelter loads (ASLs) and a high penetration of wind power is established for the PPS system. The flexibility and controllability of the VPP are improved by the demand response of the ASLs. The uncertainty associated with wind power is considered by chance constraints. The effectiveness of the proposed strategy is verified by simulation results using the data of an actual industrial power grid in Inner Mongolia, China. The DC voltage of the VSCs and the DC in the potlines of the ASLs are also investigated in the simulation.

Abstract:In the electricity market environment, the regional integrated energy system (RIES) can reduce the total operation cost by participating in electricity market transactions. However, the RIES will face the risk of load and electricity price uncertainties, which may make its operation cost higher than expected. This paper proposes a method to optimize the operation cost of the RIES in the electricity market environment considering uncertainty. Firstly, based on the operation cost structure of the RIES in the electricity market environment, the energy flow relationship of the RIES is analyzed, and the operation cost model of the RIES is built. Then, the electricity purchase costs of the RIES in the medium- and long-term electricity markets, the spot electricity market, and the retail electricity market are analyzed. Finally, considering the risk of load and electricity price uncertainties, the operation cost optimization model of the RIES is established based on conditional value-at-risk. Then it is solved to obtain the operation cost optimization strategy of the RIES. Verification results show that the proposed operation cost optimization method can reduce the operation cost of high electricity price scenario by optimizing the energy purchase and distribution strategy, constrain the risk of load and electricity price uncertainties, and help balance the risks and benefits.

Abstract:Featuring low communication requirements and high reliability, the voltage droop control method is widely adopted in the voltage source converter based multi-terminal direct current (VSC-MTDC) system for autonomous DC voltage regulation and power-sharing. However, the traditional voltage droop control method with fixed droop gain is criticized for over-limit DC voltage deviation in case of large power disturbances, which can threaten stable operation of the entire VSC-MTDC system. To tackle this problem, this paper proposes an adaptive reference power based voltage droop control method, which changes the reference power to compensate the power deviation for droop-controlled voltage source converters (VSCs). Besides retaining the merits of the traditional voltage droop control method, both DC voltage deviation reduction and power distribution improvement can be achieved by utilizing local information and a specific control factor in the proposed method. Basic principles and key features of the proposed method are described. Detailed analyses on the effects of the control factor on DC voltage deviation and imbalanced power-sharing are discussed, and the selection principle of the control factor is proposed. Finally, the effectiveness of the proposed method is validated by the simulations on a five-terminal VSC based high-voltage direct current (VSC-HVDC) system.