Abstract:Green hydrogen represents an important energy carrier for global decarbonization towards renewable-dominant energy systems. As a result, an escalating interdependency emerges between multi-energy vectors. Specifically, the coupling among power, natural gas, and hydrogen systems is strengthened as the injections of green hydrogen into natural gas pipelines. At the same time, the interaction between hydrogen and transportation systems would become indispensable with soaring penetrations of hydrogen fuel cell vehicles. This paper provides a comprehensive review for the modeling and coordination of hydrogen-integrated energy systems. In particular, we analyze the role of green hydrogen in decarbonizing power, natural gas, and transportation systems. Finally, pressing research needs are summarized.

Abstract:Due to the effects of windless and sunless weather, new power systems dominated by renewable energy sources experience power supply shortages, which lead to severe electricity shortages. Because of the insufficient proportion of controllable thermal power in these systems, this problem must be addressed from the load side. This study proposes an orderly power utilization (OPU) method with load as the primary dispatching object to address the problem of severe electricity shortages. The principles and architecture of the new urban power grid (NUPG) OPU are proposed to complete the load curtailment task and minimize the effects on social production and daily life. A flexible load baseline division method is proposed that considers the effects of factors such as gross domestic product, pollutant emission, and carbon emission to increase the flexibility and applicability of the proposed method. In addition, an NUPG OPU model based on the load baseline is proposed, in which the electric quantity balance aggregator (EQBA) serves as a regular participant in the OPU and eliminates the need for other user involvement within its capacity range. The electric quantity reserve aggregator (EQRA) functions as a supplementary participant in the OPU and primarily performs the remaining tasks of the EQBA. The electric power balance aggregator primarily offsets the power fluctuations of the OPU. Case studies demonstrate the effectiveness and superiority of the proposed model in ensuring the completion of the load curtailment task, enhancing the flexibility and fairness of OPUs, and improving the applicability of the proposed method.

Abstract:The optimal planning and operation of multi-type flexible resources (FRs) are critical prerequisites for maintaining power and energy balance in regional power grids with a high proportion of clean energy. However, insufficient consideration of the multi-dimensional and heterogeneous features of FRs, such as the regulation characteristics of diversified battery energy storage systems (BESSs), poses a challenge in economically relieving imbalance power and adequately sharing feature information between power supply and demand. In view of this disadvantage, an optimal planning and operation method based on differentiated feature matching through response capability characterization and difference quantification of FRs is proposed in this paper. In the planning stage, a model for the optimal planning of diversified energy storages (ESs) including Lithium-ion battery (Li-B), supercapacitor energy storage (SCES), compressed air energy storage (CAES), and pumped hydroelectric storage (PHS) is established. Subsequently, in the operating stage, the potential, direction, and cost of FR response behaviors are refined to match with the power and energy balance demand (PEBD) of power grid operation. An optimal operating algorithm is then employed to quantify the feature differences and output response sequences of multi-type FRs. The performance and effectiveness of the proposed method are demonstrated through comparative studies conducted on an actual regional power grid in northwest China. Analysis and simulation results illustrate that the proposed method can effectively highlight the advantages of BESSs compared with other ESs, and economically reduce imbalance power of the regional power grid under practical operating conditions.

Abstract:As renewable energy is becoming the major resource in future power grids, the weather and climate can have a higher impact on grid reliability. Transmission expansion planning (TEP) has the potential to reinforce the power transfer capability of a transmission network for climate-impacted power grids. In this paper, we propose a systematic TEP procedure for renewable-energy-dominated power grids considering climate impact (CI). Particularly, this paper develops an improved model for TEP considering climate impact (TEP-CI) and evaluates the reliability of power grid with the obtained transmission investment plan. Firstly, we create climate-impacted spatio-temporal future power grid data to facilitate the study of TEP-CI, which include the future climate-dependent renewable power generation as well as the dynamic line rating profiles of the Texas 123-bus backbone transmission (TX-123BT) system. Secondly, the TEP-CI model is proposed, which considers the variation in renewable power generation and dynamic line rating, and the investment plan for future TX-123BT system is obtained. Thirdly, a customized security-constrained unit commitment (SCUC) is presented specifically for climate-impacted power grids. The reliability of future power grid in various investment scenarios is analyzed based on the daily operation conditions from SCUC simulations. The whole procedure presented in this paper enables numerical studies on power grid planning considering climate impact. It can also serve as a benchmark for other studies of the TEP-CI model and its performance evaluation.

Abstract:The widespread adoption of renewable energy sources presents significant challenges for power system dispatching. This paper proposes a dynamic optimal power flow (DOPF) method based on reinforcement learning (RL) to address the dispatching challenges. The proposed method considers a scenario where large-scale offshore wind farms are interconnected and have access to an onshore power grid through multiple points of common coupling (PCCs). First, the operational area model of the offshore power grid at the PCCs is established by combining the prediction results and the transmission capacity limit of the offshore power grid. Built upon this, a dynamic optimization model of the power system and its RL environment are constructed with the consideration of offshore power dispatching constraints. Then, an improved algorithm based on the conditional generative adversarial network (CGAN) and the soft actor-critic (SAC) algorithm is proposed. By analyzing an improved IEEE 118-node example, the proposed method proves to have the advantage of economy over a longer timescale. The resulting strategy satisfies power system operation constraints, effectively addressing the constraint problem of action space of RL, and it has the added benefit of faster solution speeds.

Abstract:In coastal regions of China, offshore wind farm expansion has spurred extensive research to reduce operational costs in power systems with high penetration of wind power. However, frequent extreme weather conditions such as typhoons pose substantial challenges to system stability and security. Previous research has intensively examined the steady-state operations arising from typhoon-induced faults, with a limited emphasis on the transient frequency dynamics inherent to such faults. To address this challenge, this paper proposes a frequency-constrained unit commitment model that can promote energy utilization and improve resilience. The proposed model analyzes uncertainties stemming from transmission line failures and offshore wind generation through typhoon simulations. Two types of power disturbances resulting from typhoon-induced wind farm cutoff and grid islanding events are revealed. In addition, new frequency constraints are defined considering the changes in the topology of the power system. Further, the complex frequency nadir constraints are incorporated into a two-stage stochastic unit commitment model using the piece-wise linearization. Finally, the proposed model is verified by numerical experiments, and the results demonstrate that the proposed model can effectively enhance system resilience under typhoons and improve frequency dynamic characteristics following fault disturbances.

Abstract:With the rapid expansion of offshore wind farms (OWFs) in remote regions, the study of highly reliable electrical collector systems (ECSs) has become increasingly important. Post-fault network recovery is considered as an effective measure of reliability enhancement. In this paper, we propose a smart switch configuration that facilitates network recovery, making it well-suited for ECSs operating in harsh environments. To accommodate the increased complexity of ECSs, a novel reliability assessment (RA) method considering detailed switch configuration is devised. This method effectively identifies the minimum outage propagation areas and incorporates post-fault network recovery strategies. The optimal normal operating state and network reconfiguration strategies that maximize ECS reliability can be obtained after optimization. Case studies on real-life OWFs validate the effectiveness and superiority of the proposed RA method compared with the traditional sequential Monte-Carlo simulation method. Moreover, numerical tests demonstrate that the proposed switch configuration, in conjunction with proper topology and network recovery, achieves the highest benefits across a wide range of operating conditions.

Abstract:The steady-state security region (SSR) offers robust support for the security assessment and control of new power systems with high uncertainty and fluctuation. However, accurately solving the steady-state security region boundary (SSRB), which is high-dimensional, non-convex, and non-linear, presents a significant challenge. To address this problem, this paper proposes a method for approximating the SSRB in power systems using the feature non-linear converter and improved oblique decision tree. First, to better characterize the SSRB, boundary samples are generated using the proposed sampling method. These samples are distributed within a limited distance near the SSRB. Then, to handle the high-dimensionality, non-convexity and non-linearity of the SSRB, boundary samples are converted from the original power injection space to a new feature space using the designed feature non-linear converter. Consequently, in this feature space, boundary samples are linearly separated using the proposed information gain rate based weighted oblique decision tree. Finally, the effectiveness and generality of the proposed sampling method are verified on the WECC 3-machine 9-bus system and IEEE 118-bus system.

Abstract:Carbon capture and storage (CCS) systems can provide sufficient carbon raw materials for power-to-gas (P2G) systems to reduce the carbon emission of traditional coal-fired units, which helps to achieve low-carbon dispatch of integrated energy systems (IESs). In this study, an extended carbon-emission flow model that integrates CCS-P2G coordinated operation and low-carbon characteristics of an energy storage system (ESS) is proposed. On the energy supply side, the coupling relationship between CCS and P2G systems is established to realize the low-carbon economic operation of P2G systems. On the energy storage side, the concept of “ state of carbon” is introduced to describe the carbon emission characteristics of the ESS to exploit the potential of coordinated low-carbon dispatch in terms of both energy production and storage. In addition, a low-carbon economic dispatch model that considers multiple uncertainties, including wind power output, electricity price, and load demands, is established. To solve the model efficiently, a parallel multidimensional approximate dynamic programming algorithm is adopted, while the solution efficiency is significantly improved over that of stochastic optimization without losing solution accuracy under a multilayer parallel loop nesting framework. The low-carbon economic dispatch method of IESs is composed of the extended carbon emission flow model, low-carbon economic dispatch model, and the parallel multidimensional approximate dynamic programming algorithm. The effectiveness of the proposed method is verified on E14-H6-G6 and E57-H12-G12 systems.

Abstract:As the phasor measurement unit (PMU) placement problem involves a cost-benefit trade-off, more PMUs get placed on higher-voltage buses. However, this leads to the fact that many lower-voltage levels of the bulk power system cannot be observed by PMUs. This lack of visibility then makes time-synchronized state estimation of the full system a challenging problem. In this paper, a deep neural network-based state estimator (DeNSE) is proposed to solve this problem. The DeNSE employs a Bayesian framework to indirectly combine the inferences drawn from slow-timescale but widespread supervisory control and data acquisition (SCADA) data with fast-timescale but selected PMU data, to attain sub-second situational awareness of the full system. The practical utility of the DeNSE is demonstrated by considering topology change, non-Gaussian measurement noise, and detection and correction of bad data. The results obtained using the IEEE 118-bus system demonstrate the superiority of the DeNSE over a purely SCADA state estimator and a PMU-only linear state estimator from a techno-economic viability perspective. Lastly, the scalability of the DeNSE is proven by estimating the states of a large and realistic 2000-bus synthetic Texas system.

Abstract:In flexible DC grids, the rapid rise of fault current requires that the line protection must complete the fault identification within a few milliseconds. Dynamic state estimation based protection (DSEBP) provides a new idea for flexible DC line protection with good performance. However, the operating frequency in the DC grid is 0 Hz. When the DC grid is operating normally, it is difficult to identify the line parameters online to improve the performance of the protection method. This paper proposes a method to identify the frequency-dependent parameters of flexible DC grids based on the characteristic signal injection of half-bridge modular multilevel converter (HB-MMC). The characteristic signal is extracted by the Prony algorithm to calculate the line parameter under different frequencies. Afterwards, the number and position of residues and poles of frequency-dependent parameters are determined using the vector fitting method. Finally, an improved DSEBP is proposed. The simulation shows that the frequency-dependent parameters obtained by the proposed parameter identification method can be used in the improved DSEBP normally, and the identified parameters have better precision.

Abstract:The output power variability of photovoltaic (PV) power plants （PVPPs） is one of the major challenges for the operation and control of power systems. The short-term power variations, mainly caused by cloud movements, affect voltage magnitude and frequency, which may degrade power quality and power system reliability. Comprehensive analyses of these power variations are crucial to formulate novel control approaches and assist power system operators in the operation and control of power systems. Thus, this paper proposes a simulation-based approach to assessing short-term power variations caused by clouds in PV power plants. A comprehensive assessment of the short-term power variations in a PV power plant operating under cloud conditions is another contribution of this paper. The performed analysis evaluates the individual impact of multiple weather condition parameters on the magnitude and ramp rate of the power variations. The simulation-based approach synthesizes the solar irradiance time series using three-dimensional fractal surfaces. The proposed assessment approach has shown that the PVPP nominal power, timescale, cloud coverage level, wind speed, period of the day, and shadow intensity level significantly affect the characteristics of the power variations.

Abstract:The performance of photovoltaic (PV) systems is influenced by various factors, including atmospheric conditions, geographical locations, and spatial and temporal characteristics. Consequently, the optimization of PV systems relies heavily on the global maximum power point tracking (GMPPT) methods. In this paper, we adopt virtual reality (VR) technology to visualize PV entities and simulate their performances. The integration of VR technology introduces a novel spatial and temporal dimension to the shading pattern recognition (SPR) of PV systems, thereby enhancing their descriptive capabilities. Furthermore, we introduce an interactive GMPPT (IGMPPT) method based on VR technology. This method leverages interactive search techniques to narrow down search regions, thereby enhancing the search efficiency. Experimental results demonstrate the effectiveness of the proposed IGMPPT in representing the spatial and temporal characteristics of PV systems and improving the efficiency of GMPPT.

Abstract:The integration of photovoltaic power generation is a new development into the traction power supply system (TPSS). However, traditional research on the TPSS operation strategy has not fully considered the risk of uncertainty in photovoltaic power output. To this end, we propose an operation strategy for the rail transit green energy system that considers the uncertainty risk of photovoltaic power output. First, we establish a regenerative braking energy utilization model that considers the impact of time-of-use (TOU) electricity price on the utilization efficiency and economic profit of regenerative braking energy and compensates for non-traction load. Then, we propose an operation strategy based on the balance of power supply and demand that uses an improved light robust (ILR) model to minimize the total cost of the rail transit green energy system, considering the risk of uncertainty in photovoltaic power output. The model incorporates the two-step load check on the second-level time scale to correct the operational results, solve the issue of different time resolutions between photovoltaic power and traction load, and achieve the coordinated optimization of risk cost and operation cost after photovoltaic integration. Case studies demonstrate that the proposed model can effectively consider the impact of the uncertainty in photovoltaic power output on the operation strategy, significantly improving the efficiency and economy of the system operation.

Abstract:This study introduces a mixed-integer second-order conic programming (MISOCP) model for the effective management of load and energy in active distribution networks featuring prosumers. A multi-objective function is devised to concurrently minimize various costs, including prosumer electricity costs, network energy loss costs, load shedding costs, and costs associated with renewable energy resource outages. The methodology involves determining optimal active power adjustment points for photovoltaic (PV) resources and integrated energy storage systems (ESSs) within network buildings, in conjunction with a demand-side management program. To achieve the optimal solution for the proposed MISOCP model, a robust hybrid algorithm is presented, integrating the modified particle swarm optimization (MPSO) algorithm and the genetic algorithm (GA). This algorithm demonstrates a heightened capability for efficiently converging on challenging problems. The proposed model is evaluated using a distribution network comprising 33 buses, a practical distribution network, and a distribution network comprising 118 buses. Through comprehensive simulations in diverse cases, the results highlight the innovative contributions of the model. Specifically, it achieves a noteworthy reduction of 26.2% in energy losses and a 17.72% decrease in voltage deviation. Additionally, the model proves effective in augmenting prosumer electricity sales, showcasing its potential to improve the overall efficiency and sustainability of active distribution networks.

Abstract:The large-scale penetration of photovoltaic (PV) units and controllable loads such as electric vehicles (EVs) render the distribution networks prone to frequent, uncertain, and simultaneous over/under voltages. The coordinated control of devices such as on-load tap changer (OLTC), PV inverters, and EV chargers seem efficient in regulating the distribution network voltage within normal operation limits. However, the need for measuring infrastructure throughout the distribution network and communication setup to all control devices makes it practically and economically difficult. Furthermore, for large networks, the large measurement dataset of the network and distributed control resources increase the computational complexity and the response time. This paper proposes a voltage control strategy based on dual-stage model predictive control by coordinating devices such as OLTC and controllable PVs and EV charging stations. A minimum set of available control resources is identified to establish the voltage control in the network with reduced communication and minimum measuring infrastructure, using a reduced model framework. Simulations are performed on 33-bus distribution network and the modified IEEE 123-bus distribution network to validate the efficacy of the proposed control strategy.

Abstract:High penetration of renewable energy sources (RESs) induces sharply-fluctuating feeder power, leading to voltage deviation in active distribution systems. To prevent voltage violations, multi-terminal soft open points (M-SOPs) have been integrated into the distribution systems to enhance voltage control flexibility. However, the M-SOP voltage control recalculated in real-time cannot adapt to the rapid fluctuations of photovoltaic (PV) power, fundamentally limiting the voltage controllability of M-SOPs. To address this issue, a full-model-free adaptive graph deep deterministic policy gradient (FAG-DDPG) model is proposed for M-SOP voltage control. Specifically, the attention-based adaptive graph convolutional network (AGCN) is leveraged to extract the complex correlation features of nodal information to improve the policy learning ability. Then, the AGCN-based surrogate model is trained to replace the power flow calculation to achieve model-free control. Furthermore, the deep deterministic policy gradient (DDPG) algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate model. Numerical tests have been performed on modified IEEE 33-node, 123-node, and a real 76-node distribution systems, which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPG model.

Abstract:With the integration of wind power, photovoltaic power, gas turbine, and energy storage, the novel battery charging and swapping station (NBCSS) possesses significant operational flexibility, which can aid in the service restoration of distribution system (DS) during power outages caused by extreme events. This paper presents an integrated optimization model for DS restoration that considers NBCSS, repair crews, and network reconfigurations simultaneously. The objective of this model is to maximize the restored load while minimizing generation costs. To address the uncertainties associated with renewable energies, a two-stage stochastic optimization framework is employed. Additionally, copula theory is also applied to capture the correlation between the output of adjacent renewable energies. The conditional value-at-risk (CVaR) measure is further incorporated into the objective function to account for risk aversion. Subsequently, the proposed optimization model is transformed into a mixed-integer linear programming (MILP) problem. This transformation allows for tractable solutions using commercial solvers such as Gurobi. Finally, case studies are conducted on the modified IEEE 33-bus and 69-bus DSs. The results illustrate that the proposed method not only restores a greater load but also effectively mitigates uncertainty risks.

Abstract:This paper highlights the inefficiency of most distributed controls in dealing with dynamic enhancement while coordinating distributed generators (DGs), leading to poor frequency dynamics. To address this concern, a two-level coupling-based frequency control strategy for microgrids is proposed in this paper. At the lower level, an adaptive dynamic compensation algorithm is designed to tackle short-term and long-term frequency fluctuations caused by the uncertainties of renewable energy resources (RESs). At the upper level, an adaptive distributed frequency consensus algorithm is developed to address frequency restoration and active power sharing. Furthermore, to account for the potential control interaction of the two designed levels, a nonlinear extended state observer (NESO) is introduced to couple their control dynamics. Simulation tests and hardware-in-the-loop (HIL) experiments confirm the improved frequency dynamics.

Abstract:This paper focuses on the distributed control problem in a networked microgrid (NMG) with heterogeneous energy storage units (HESUs) in the environment considering multiple types of time delays, which include the state, input, and communication delays. To address this problem, a state feedback control (SFC) strategy based on nested predictor is proposed to mitigate the influence of multiple types of time delays. First, a distributed control method founded upon voltage observer is developed, which can realize proportional power distribution according to the state of charge (SOC) of the HESUs, while adjusting the average voltage of the point of common coupling (PCC) bus in the NMG to its rated value. Then, considering that there exists steady-state error resulting from the initial value of the observer and impact of time delays, an SFC strategy is proposed to further improve the robustness of the NMG against time delays. Finally, the experimental results demonstrate that the proposed distributed control method is capable of fully compensating for the state, input, and communication delay. Moreover, the NMG exhibits remarkable resistance to multiple types of time delays, which has higher reliability and robustness.

Abstract:Considering the majority of electrical equipment utilized in society is driven by DC, integrating a DC system can significantly enhance the efficiency and reliability of power systems by implementing the integration of diverse loads, renewable energy sources (RESs), and energy storage systems (ESSs). In this paper, the integration of multiple DC zero-carbon buildings (DC-ZCBs) is proposed to achieve the unbalanced voltage suppression of the bipolar DC microgrid (DCMG). The photovoltaic (PV) technology, loads, and DC electric springs (DC-ESs) are adopted as a unified entity to achieve the zero-carbon emission of the building. Firstly, a new configuration of PV and DC-ESs is introduced. The energy management of PV, ESS, and load are fully considered in this new configuration, which can reduce the capacity of the ESS. Subsequently, a distributed cooperative control strategy for DC-ESs based on the modulus voltage is presented, which is implemented with integration of the new configuration into the bipolar DCMG. The proposed approach addresses the issues of unbalanced voltage to improve the operating efficiency and power quality of the bipolar DCMG. The simulation is conducted in MATLAB/Simulink platform to confirm the effectiveness of the proposed approach.

Abstract:With the goal of low-carbon energy utilization, electric vehicles (EVs) and EV charging stations (EVCSs) are becoming increasingly popular. The economical operation strategy is always a primary concern for EVCSs, while users ’ behavior and operating data leakage problems in EVCSs have not been taken seriously. Herein, federated deep reinforcement learning, a privacy-preserving method, is applied to learn the optimal strategy for multiple EVCSs. However, it is prone to Byzantine attacks. It is urgent to achieve an economical operation strategy while preserving data privacy and defending against Byzantine attacks. Therefore, this paper proposes a Byzantine-resilient federated deep reinforcement learning (BR-FDRL) method to address these problems. First, the distributed EVCS data are utilized by the federated deep reinforcement learning to train an economical operation strategy while preserving privacy by only transmitting gradients. The sampling efficiency is enhanced by both federated learning and stochastically controlled stochastic gradient. Then, the Byzantine-resilient gradient filter (BRGF) designs two distance rules to keep malicious gradients out. The case study verifies the effectiveness of the proposed BRGF in resisting Byzantine attacks and the effectiveness of federated deep reinforcement learning in improving convergence speed and reward and preserving privacy. The resluts show that the BR-FDRL method minimizes the operation cost by an average of 35% compared with the rule-based method while meeting the state of charge demand as much as possible.

Abstract:To improve the safety of the solid oxide fuel cell (SOFC) systems and avoid the generation of large amounts of pollutants during power switching, this paper designs a power switching strategy based on trajectory planning and sliding mode control (TP-SMC). The design elements of the power switching strategy are proposed through simulation analysis at first. Then, based on the gas transmission delay time and the change of gas flow obtained from testing, trajectory planning (TP) is implemented. Compared with other power switching strategies, it has been proven that the power switching strategy based on TP has significantly better control performance. Furthermore, considering the shortcomings and problems of TP in practical application, this paper introduces sliding mode control (SMC) on the basis of TP to improve the power switching strategy. The final simulation results also prove that the TP-SMC can effectively suppress the impact of uncertainty in gas flow and gas transmission delay time. Compared with TP, TP-SMC can ensure that under uncertain conditions, the SOFC system does not experience fuel starvation and temperature exceeding limit during power switching. Meanwhile, the NOx emissions are also within the normal and acceptable range. This paper can guide the power switching process of the actual SOFC systems to avoid safety issues and excessive generation of NOx, which is very helpful for improving the performance and service life of the SOFC systems.

Abstract:The inter-regional electricity market is instrumental in enhancing the economic efficiency, reliability, and integration of renewable generation within interconnected power systems. As the market boundary expands, the complexity and solution difficulties of market clearing increase rapidly. The presence of hybrid alternating current (AC)/direct current (DC) interconnector networks further compounds challenges in modeling trading paths and transmission tariffs. To address these issues, this paper proposes a path-aware market-clearing (PAMC) model tailored for the inter-regional electricity market, which accommodates the hybrid AC/DC interconnector network. A variable aggregation strategy is proposed to reduce the problem scale while ensuring equivalent optimality. In addition, a novel redundancy elimination method is developed to expedite the solution of the market-clearing problem. This framework utilizes envelope approximations of residual demand curves to identify bidding blocks that will not affect the marginal price. Corresponding decision variables are then constrained to their bounds to remove redundant information. Comprehensive case studies across different power system scales validate the superiority of the proposed PAMC model in improving social welfare, and verify the effectiveness of the proposed redundancy elimination method in accelerating the solution of the market-clearing problem.

Abstract:Nowadays, public policies in Chile are geared towards the promotion of distributed energy resources (DERs) such as distributed photovoltaic (PV) systems. However, the prevailing socioeconomic context and the lack of incentive to invest in DERs have posed a challenge to achieving the established goals in the coming years. This paper develops a three-entity architecture model and decision-making algorithms for peer-to-peer (P2P) PV energy trading. It seeks to conduct a sensitivity analysis of a P2P PV energy trading system in a community microgrid, to assess the potential benefits for local communities and to encourage the development of new local public policies aimed at enhancing the profitability of DERs. Various scenarios are compared, both with and without P2P market, considering residential customers (RCs), encompassing both consumers and prosumers with PV systems, with or without battery energy storage systems (BESSs), an aggregator (AG), and utility grid (UG). Daily energy and economic transactions are examined with the aim of reducing the annual electricity bills for each RC, enhancing the profitability of DERs for prosumers, increasing incomes for the AG, and exploring potential benefits for the UG. The load profiles and meteorological data are collected from publicly available databases, and a novel electricity pricing scheme is proposed based on current rates offered by the local UG. The results demonstrate that the P2P market could lead to a reduction in the annual electricity bills by as much as 1.76% for consumers, an increase in annual income of up to 149% for prosumers, and a reduction in the payback period for their DERs by up to 0.4 years. This paper contributes to improving the investment in DER projects and provides a guide for extending the work to different regions of Chile and global emerging economies with DER potential.

Abstract:Considering the enormous potential application of autonomous mobility-on-demand (AMoD) systems in future urban transportation, the charging behavior of AMoD fleets, as a key link connecting the power system and the transportation system, needs to be guided by a reasonable charging demand management method. This paper uses game theory to investigate charging pricing methods for the AMoD fleets. Firstly, an AMoD fleet scheduling model with appropriate scale and mathematical complexity is established to describe the spatio-temporal action patterns of the AMoD fleet. Subsequently, using Stackelberg game and Nash bargaining, two game frameworks, i.e., non-cooperative and cooperative, are designed for the charging station operator (CSO) and the AMoD fleet. Then, the interaction trends between the two entities and the mechanism of charging price formation are discussed, along with an analysis of the game implications for breaking the non-cooperative dilemma and moving towards cooperation. Finally, numerical experiments based on real-world city-scale data are provided to validate the designed game frameworks. The results show that the spatio-temporal distribution of charging prices can be captured and utilized by the AMoD fleet. The CSO can then use this action pattern to determine charging prices to optimize the profit. Based on this, negotiated bargaining improves the overall benefits for stakeholders in urban transportation.

Abstract:Non-intrusive load monitoring (NILM) can provide appliance-level power consumption information without deploying submeters for each load, in which load event detection is one of the crucial steps. However, the existing event detection methods do not efficiently detect both the starting time of an event (STE) and the ending time of an event (ETE), and their adaptability to scenarios with different sampling rates is limited. To address these problems, in this paper, an event detection method based on robust random cut forest (RRCF) algorithm, which is an unsupervised learning method for detecting anomalous data points within a dataset, is proposed. First, the mean-pooling preprocessing is applied to the aggregated load power series with a high sampling rate to minimize fluctuations. Then, the power differential series is obtained, and the anomaly score of each data point is calculated using the RRCF algorithm for preliminary detection. If an event has been preliminarily detected, misidentification caused by fluctuation will be further eliminated by using an adaptive power difference threshold approach. Finally, linear fitting is used to finely and accurately adjust the STE and ETE. The proposed method does not require any pretraining of the detection model and has been validated with both the BLUED dataset (with high and low sampling rates) and the REDD dataset (with low sampling rate). The experimental results demonstrate that the proposed method not only meets real-time requirements, but also exhibits strong adaptability across multiple scenarios. The precision is greater than 92% in distinct sampling rate scenarios, and the F1 score of phase B on the BLUED dataset reaches 94% in the scenario with a high sampling rate. These results indicate that the proposed method outperforms other state-of-the-art methods.

Abstract:As transmission power among interconnected regional grids is increasing rapidly, formulating the power distribution and maintenance schedules of multiple paralleled transmission channels are critical to ensure the secure and economic operation in an AC/DC power system. A coordinated optimization for power distribution and maintenance schedules (COPD-MS) of multiple paralleled transmission channels is proposed, and the active power losses of the resistances of earth line in the high-voltage direct current (HVDC) transmission lines are taken into account when one pole is under maintenance while the other pole is operating under monopolar ground circuit. To solve the proposed COPD-MS model efficiently, the generalized Benders decomposition (GBD) algorithm is used to decompose the proposed COPD-MS model into master problem of maintenance scheduling and sub-problems of power distribution scheduling, and the optimal solution of the original model is obtained by the alternative iteration between them. Moreover, a recursive acceleration (RA) algorithm is proposed to solve the master problem, which can directly obtain its solution in the new iteration by using the solution in the last iteration and the newly added Benders cut. Convex relaxation techniques are applied to the nonlinear constraints in the sub-problem to ensure the reliable convergence. Additionally, since there is no coupling among the power distributions during each time interval in the sub-problem, parallel computing technology is used to improve the computational efficiency. Finally, case studies on the modified IEEE 39-bus system and an actual 1524-bus large-scale AC/DC hybrid power system demonstrate the effectiveness of the proposed COPD-MS model.

Abstract:During the power modulation process of line commutated converter-based high-voltage direct current (LCC-HVDC), the transient power mismatch between the fast-change converter station and the slow-response reactive power compensators (RPCs) can cause transient voltage disturbances at the weak sending end of the AC grid. To mitigate such voltage disturbances, this paper proposes a coordinated feedback power control method for the hybrid multi-infeed HVDC (HMI-HVDC) system comprising an LCC-HVDC and voltage source converter-based HVDC (VSC-HVDC) systems. The mechanism of the disturbance caused by transient power mismatch is quantitatively analyzed, and the numerical relationship between the instantaneous unbalanced power and the AC voltage is derived. Based on the numerical relationship and considering the time-varying relationship of reactive power between converter stations, the unbalanced power is set as the feedback and coordinately distributed among the inverter stations of VSC-HVDC, and the rectifier and the inverter stations of LCC-HVDC. Simulation results verify that the proposed method can effectively suppress voltage disturbance without relying on remote communication, thus enhancing the operation performance of the HMI-HVDC system.

Abstract:Mid- and high-frequency resonance (MHFR) is highly likely to occur at the sending end of voltage source converter-based ultra-high voltage direct current (VSC-UHVDC) for large-scale renewable energy transmission. It is of great importance to investigate the resonance characteristics and the corresponding suppression strategies. Firstly, this paper introduces the overall control scheme of VSC-UHVDC for large-scale renewable energy transmission. Then, the impedance models of VSC under grid-forming control with AC voltage coordinated control are established. The mid- and high-frequency impedance characteristics of VSC-UHVDC are analyzed. The key factors affecting the impedance characteristics have been revealed, including the AC voltage control, the voltage feedforward, the inner current loop, the positive-sequence and negative-sequence independent control (PSNSIC), and the control delay. The MHFR characteristics at the sending-end system are analyzed in the whole operation process, including the black start and the normal power transmission operation. An integrated control scheme is proposed to address the MHFR problems. Finally, extensive case studies are conducted on a planned VSC-UHVDC project to verify the theoretical analysis.

Abstract:The capacitor is one of the most important components in a modular multilevel converter (MMC). Due to the chemical process and the aging effect, the capacitor is subject to deterioration over time which is usually manifested by a drop in capacitance. To identify the abnormal capacitors and enhance the reliability of MMCs, an improved submodule (SM) capacitor condition monitoring method is proposed in this paper. The proposed method estimates the capacitance during each control cycle based on the switching states of SMs, offering advantages such as high accuracy and no adverse influence on the operation of MMCs. Firstly, the aging differences of capacitors in different SMs per arm of MMC are analyzed. Then, the capacitances of SMs that switch on the state are calculated based on the relationship between the capacitor voltage and current during each control cycle. A data processing algorithm is proposed to improve the accuracy of capacitance estimation. Finally, the simulation and the real-time control hardware-in-the loop test results based on real-time digital simulator (RTDS) show the effectiveness of the proposed method.

Abstract:The rapid development of the power system requires high reliability and real-time situational awareness of power equipment. The current agent-based condition-monitoring perception mode is not suitable for widely distributed power equipment due to the potential of single-point failure and high communication and data costs. Therefore, the technical development path of the power equipment perception mode is analyzed based on the development trend of the future power system. The concept of intelligent power equipment (IPE) is introduced, which combines online sensing, data mining, remote communication, and primary and secondary fusion technologies to develop an intelligent object that can realize autonomous situational awareness. IPE can actively interact with the control center and operation and maintenance (O&M) personnel according to its situation. This gives the power company an efficient and comprehensive perception of the equipment. Then, based on the actual situation of the power grid and emerging technology research directions, the challenges faced by each key technology supporting IPE and the corresponding technology enhancement solutions are presented. In addition, the O&M method applicable to IPE is discussed, which achieves proactive maintenance and prognosis management through autonomous equipment perception. Finally, the feasibility and effectiveness of IPE are verified by the performance of current IPE applications in an actual power grid.

Abstract:Photovoltaic (PV) generation always exhibits strong uncertainty and variability; therefore, its excessive integration brings huge risks to the safe operation of power systems. In this letter, a two-stage robust optimization approach based on decision-dependent uncertainty is devised to identify the PV hosting capacity that can be accepted to ensure the effective consumption of PV generation under uncertainty. The proposed approach is validated by numerical experiments for a microgrid and a distribution network.