The Smart Grid Operation Centre (SGOC) is established on 1st April, 2021, as part of the purpose-built facilities of the Haking Wong Campus of the Hong Kong Institute of Vocational Education (IVE), a Vocational Training Council (VTC) member institution. With the generous contributions and steadfast support from CLP Power Hong Kong Limited (CLP Power), SGOC is served as simulation and training facility available to support all Smart Grid development in Hong Kong. The SGOC has installed the Real-Time Digital Simulator (RTDS) and Smart grid-related equipment, including the Distributed Energy Resources (DERs), protection relay, and smart meter that can be looped into the simulator, for its capability and interoperability on real-time studies, including modeling and resolving the potential issues in realtime before they may impact the delivery or operation of the project or the smart grid network. Facing the up-coming challenges with penetration of renewable energy, digitalization, and increasing of electric vehicles, the SGOC provides a feasible range of smart grid related services in respect of real-time studies on the operation of smart grid system, operator training, and operational support that enable derisk deployment and operation of the related smart grid parties, and more practical and tailored training which increases familiarity and confidence of operators. This paper describes the practicalities of specifying, testing, installation and interfacing the platform, with a realtime simulator, at the SGOC, for future operational training and support use, including some uses on faults during operation, diagnosing alarms, scheme updates/upgrades and network interactions and range of smart grid focused training courses will be available and content enhancement into course delivery. Through these wide-ranging installations and systems, trainers will learn to apply the latest industry technologies. This will enrich their knowledge of smart grids and relevant new technologies, thereby helping to promote smart, clean energy development. This paper is also included the Power Hardware- In-the-Loop (PHIL) platform and a case study introduction for the frequency capability for microgrid and Battery Energy Storage System (BESS) response.
Permanent magnet synchronous generators (PMSGs) are widely used nowadays in wind power integration. In addition, as more and more large-capacity wind turbines are connected to the power grid, the machine-side dynamics, especially the shaft torsional vibration problem has been received extensive attention. This paper firstly provides a two-open-loop two-mass shaft subsystem model for machine-side dynamics of PMSG. Then a damping torque analysis is introduced to investigate the impact mechanism of machine-side converter (MSC) on the shaft torsional oscillatory dynamics of wind turbine mass. The torsional oscillatory dynamics of the two-mass model are investigated with modal analysis, and the relationship between the oscillation modes and related participation factors are clarified. The effectiveness of the two-open-loop two-mass shaft subsystem model and the proposed damping torque analysis are demonstrated and evaluated in PMSG based wind generation system.
The newly installed scale of distributed photovoltaic is far larger than that of centralized photovoltaic, so the related research work of distributed photovoltaic has attracted a wide spread attention. However, the existing simulation model of distributed photovoltaic power station only focuses on single operating condition, which cannot meet the requirements of simulation and the analysis of distributed photovoltaic power station at all operating conditions. In order to overcome the above defects, this paper presents a simulation model under all operating conditions based on the PSCAD/EMTDC. Meanwhile it can provide a model basis for studying the stability and control problems of distributed photovoltaic power station under different operating conditions. Firstly, photovoltaic array is established according to engineering mathematical model. Secondly, the control strategies under different working conditions are studied, and the photovoltaic inverter model with all operating conditions control strategy is established. Thirdly, distributed photovoltaic power station is built by current-doubler method.Finaly, three scenarios are simulated to verify the output characteristic under different operating conditions.The simulation results show that all operating conditions simulation model of distributed photovoltaic power station based on PSCAD can achieve the expected operation effect.
The system inertia, H ( t ) and the rotor angle, δ ( t ) play an important role in the stability study of a power grid, with the latter dictating the active power being delivered to the grid. Since δ ( t ) is a changing parameter within the power system, its nonlinear dynamic behaviour can prove to be very challenging to be studied. In the present work, we establish an ordinary differential equation (ODE) which gives the temporal variation of H ( t ) as a function of δ ( t ). We show that the solution of the ODE is bounded under certain conditions on the mechanical power input, Pm and the electrical power output Pmax of the generator. Furthermore, in this preliminary work, we also prove that the solution grows unbounded when δ ( t ) is close to certain points. A numerical experiment is conducted to solve the swing equation together with the derived ODE, using Runge-Kutta 4th order method, to verify the derivations. The current preliminary results confirm that H ( t ) can be evaluated based on δ ( t ), hence confirming the significance of the rotor angle in a power system.
Mobile energy storage (MES) has the flexibility to temporally and spatially shift energy, and optimal configuration of MES shall significantly improve the active distribution network (ADN) operation economy and renewables consumption. In this paper, an optimal planning model of MES is established for ADN with a goal of maximizing the annual revenue of MES. Firstly, the annual revenue of MES is set up with consideration of the investment cost and operation cost of MES, wind and PV curtailment cost, network loss cost and the peak-valley arbitrage income of MES. Then, the distributed photovoltaic and wind power access constraints, power conservation constraints of ADN, energy coupling and displacement constraints of MES are further tailored to establish the MES planning model. Afterwards, the proposed model is solved by the second order cone relaxation combined with the large M algorithm. Finally, simulation results of the modified IEEE 33-bus distribution network validate the effectiveness of the proposed model.
In the context of “30 60” double carbon target, the decarbonization of the power system is the key to the zero-carbon development of the whole society. The complementarity of energy storage and wind generation can effectively alleviate the uncertainty and volatility of renewable energy and promote zero-carbon energy integration. In this paper, an optimal bidding strategy for Wind Storage Combined System (WSCS) to participate in the electricity-carbon combined market is proposed Firstly, the market structure and market operation mode of China's current carbon trading market are introduced. On this basis, the trading methods, trading processes and clearance mechanisms of WSCS participating in the combined electricity-carbon market are analyzed. Secondly, based on the master-slave game, a two- layer bidding strategy model for WSCS to participate in the electricity-carbon combined market is proposed. Finally, through the example analysis, it is shown that the introduction of carbon trading mechanism has improved the competitiveness of WSCS in the electricity market on the one hand, and raised its own economic benefits by a notch; On the other hand, while increasing the total consumption of renewable energy, it reduces the total amount of regional carbon emissions.
This paper aims to improve the energy management efficiency of home microgrids while preserving privacy. The proposed microgrid model includes energy storage systems, PV panels, loads, and the connection to the main grid. A federated multi-objective deep reinforcement learning architecture with Pareto fronts is proposed for total carbon emission and electricity bills optimization. The privacy of data is protected by federated learning, by which the original data will not be uploaded to the server. Numerical results show that compared with the traditional single Deep-Q network, using the proposed method the accumulated carbon emission decreased by 3% and the electricity bills decreased by 21%.
The stray current of DC metro flowing into the transformer of urban power grid leads to the increase of neutral direct current (NDC) and the occurrence of DC bias of the transformer. Due to the influence of DC metro operation characteristics, the NDC fluctuates randomly and alternates positively and negatively. The distribution characteristics of NDCs are complex and indistinct. In order to obtain the distribution characteristics of NDCs among various transformers. Authors have measured the NDCs of transformers in Shenzhen power system. Then the distribution characteristics of NDCs are analysed by correlation analysis during various periods. The results show that among the transformers near the metro, the NDCs of the transformers supplying to the metro and near the metro depot are large, the NDCs directions between the 500kV transformers and the directly connected 220kV transformers are opposite and there is an obvious negative correlation among the 500kV and 220kV transformers.
Modular multilevel converters(MMC) are widely used in power systems. However, relevant studies and reports show that MMC may cause oscillation problems by connecting with the grid. The second-harmonic circulating current is caused for the energy exchange between MMC phases under the current conversion process. The unreasonable parameter design of converter will lead to the natural frequency of the MMC approaching the frequency-doubled of grid. Farther, it may cause the bridge arm current oscillation. When the bridge arm resistance is large enough, the operating conditions will be stable for the overdamping in the system. Virtual damping control strategy is applied to stability study, which can improve the damping of the system without increasing the system loss and suppress the oscillation. Therefore, a stability control strategy base on virtual damping is designed to improve the stability margin of the system. In order to verify the effectiveness of the method, the main circuit model of MMC is established by using the form of matrix. Besides, it considers the virtual damping control. In order to verifie the effectiveness of the virtual damping control, the MMC simulation model is established under the Matlab/Simulink . The results show that the proposed virtual damping control can effectively suppress the MMC oscillation problem.
Transmission loss, as one of the major factors affecting the adequacy and economy of the electricity supply, has been analyzed and optimized in the modern power system. Under the scenario of carbon neutrality and the low-carbon power system, the carbon emission induced by transmission loss is also important, highlighting the necessity of carbon emission reduction in power systems. This paper proposes a novel carbon emission optimization method, aiming to reduce the carbon emission induced by transmission loss. Given the generator carbon intensity (GCI), the corresponding carbon intensity at each node can be calculated via the carbon emission flow (CEF) analysis, a CEF model can be built based on which by transforming power-related parameters to carbon-related parameters, converting the traditional power flow optimization into CEF optimization. By comparing the results of the carbon emission before and after optimization, it is demonstrated that the proposed carbon emission optimization method can reduce carbon emission significantly.
The wind power forecasting technology can be an important basis for wind power grid connection and the power system dispatching department to make wind power dispatching plans. Firstly, for the drawback that BP neural network is prone to fall into local optimum, the simulated annealing algorithm is introduced to optimize the initial weights and thresholds of the BPNN to construct SA_BP prediction model. Secondly, VMD was implemented to decompose the raw wind speed series into a number of sub-series and reconstruct the subsequence based on the sample entropy. Finally, the reconstructed components are predicted separately by SA_BP and the predictions are then stacked to get the ultimate forecast results. To prove the proposed prediction model's validity, wind speed prediction of a wind farm was simulated and three other prediction models were compared through four indicators: mean square error, root mean square error, mean absolute error and mean absolute percentage error. The experimental data indicate that compared to the BPNN prediction model, the VMD_SA_BP prediction model has a 2.576 lower mean square error, 0.9535 lower root mean square error, 0.6913 lower mean absolute error and 4.5101 percentage points lower mean absolute percentage error.
Fault and /or Disturbances are quite common in aged network while protection initiated switching of primary devices play a major role in isolating them and avoid spreading elsewhere. While a single fault is unlikely to cause a black out, hidden failure of primary and secondary power system, equipment or human errors would cause a simple circuit fault into series of tripping called “cascaded tripping”. Al Ain Distribution Network (AADC), an affiliate of TAQA, a major power distribution utility in UAE, covers an area of 11000 in sq. km region of Abu Dhabi Emirate, encompassing several 33/11 kV Primary and 11/0.4 kV distribution substations. Eliminating and/or mitigating the cause of failures is a major challenge to AADC in view of achieving customer satisfaction and meeting regulatory demands. AADC formed a Root Cause Analysis (RCA) team to assess such tripping happened in previous 3 years, with a view to assess the prime cause of such failures, thereby creating plans and implement them to overcome such outages in future. Automation strategies form a vital role in monitoring the network, thereby minimizing such anomalies. Overall, such phenomenon is overcome by implementing operational measures, enhance the maintenance activities of the protection system and the power equipment through CBM (Condition Based Maintenance), with the aid of Distribution System Monitoring.
Guizhou Province is an important energy base for the "west-to-east power transmission" in China. Beneficial from the strategic goal of "carbon peaking and carbon neutrality" proposed by the Chinese government, the new energy such as wind and photovoltaic power in Guizhou is stepping to an expressway gradually. The equipped capacitor of renewable generation is aim to add 23.3 GW during the period of "Fourteenth Five-Year Plan" and expand to 40.0 GW by 2025, accounting for about 36.2% of the total equipped capacity of renewable generation. For the characteristic such as intermittent, fluctuating and random, etc. of the output of new energy, the large-scale grid connection will introduce new challenges to the safe and continuous operation of power system. In order to meet the needs of multi-scenario applications such as peak regulation and frequency modulation of new power system in Guizhou Province, this paper proposed a scheme of improvement of regulation ability through researching regulation ability improving methods, such as flexible transformation of coalfired power, hydro-power expansion, pumped storage and new energy storage system (ESS) construction, etc. The results show that by 2025, in Guizhou Province, about 10% of the new ESS capacity will be installed on the power source side. Independent ESS projects will be built according to the peak shaving needs of the power system on the grid side, and several new ESS projects are advised for demonstration purposes on the load side due to it is difficult to recover the cost of ESS by relying only peak-valley price difference. This research results can work as a decision-making reference for the improvement of regulation capacity of new power system and the planning t of new energy storage in Guizhou Province.
Due to the pressure of the worldwide governments on environmental protection, the power supply mode of conventional buildings is meeting a continuous change. In order to reduce carbon dioxide emission, the green building, whose energy supply system has ph otovoltaic (PV) generation and energy storage (ES) integration, has received much attention. Different from the conventional building energy system, the coordination of the PV and ES may significantly influence the economy and operation stability of the gr een building operation, especially when the green building operates under an electric market. In this paper, an optimized control strategy for green buildings with PV and ES integration is proposed considering the electricity prices. The objective of the p roposed optimized control strategy is to minimize the electric cost while maintaining the operation stability of the green building. Firstly, the battery charging strategy and the optimal system operation strategy are analyzed under the normal conditions o f power generation and consumption. Then, according to the probability distribution of the power generation curves and power consumption curves, some typical scenes are divided for the modeling of the optimal economic model. Thirdly, the optimal economic m odel is reorganized by involving the active load, such as interruptible load and non interruptible load. Finally, the proposed optimized control strategy is solved from the optimal economic model in order to realize the regulation of energy storage and PV. The effectiveness of the proposed strategy is verified by the case studies.
Event-driven emergency control (EEC) executes control actions immediately following a risky disturbance, which serves as an effective and efficient scheme to restore power system stability and prevent cascading events. With the increasing variational energy sources in the power grid, battery energy storage systems (BESS) have been widely deployed in the system for response-driven control services such as primary frequency control, but the role of BESS in EEC is yet to be recognized. Viewing BESS as a type of switching power source, this paper aims to investigate the capability of BESSs to participate in power system EEC through immediate tripping. A hierarchical control method considering the deviated control costs among different types of control actions, including BESS shedding and load shedding, is proposed, where trajectory sensitivity analysis is used in a hierarchical way to efficiently decide the optimal control strategy. The proposed method has been tested on an IEEE 39-bus system integrated with widespread renewable energy sources (RES) and BESSs for event-driven emergency control. It is demonstrated that the proposed method can well utilize the available BESS resources to reduce the control burden and costs of load shedding while meeting the required stability criteria, which validates the efficacy of BESSs in event-driven emergency control of renewable power systems.
With the development of power electronics and motor control, AC motor speed control system or servo system has been widely explored. Among them, the induction motor driving based on three-phase six-switch inverter has become one of the hottest topics. However, in certain applications, people want to further strengthen the cost savings. At the same time, the driving system is desired to have the fault-tolerant ability. Under the circumstance, the motor operation mode based on the three-phase four-switch inverter came into being. However, it has fewer voltage vectors, with unequal amplitudes, spatial asymmetry, and no zero vector, which will inevitably lead to the deterioration of the dynamic and static performance. In this paper, the theory of four-switch motor operation mode and its control performance is deeply analyzed. The model predictive direct torque control algorithm is proposed in order to obtain better static and dynamic characteristics.
In view of the problems of single detection method, insufficient statistical information, difficult standardization of classification methods, and low timeliness that exist in the massive increase of network assets in the power system, improve the identification and monitoring accuracy of network asset information and the real-time management of network assets. The rational allocation of resources is very important. To this end, this paper proposes a research on network asset fingerprinting based on CNN-GRU neural network model. This paper closely combines the security protection requirements of power grid network operation and maintenance, takes network asset fingerprints as the research object, and uses the characteristics of CNN-GRU neural network model. Asset fingerprint identification is carried out accurately, and the experimental results of the network asset fingerprint identification technology proposed in this paper are analyzed. The experimental results show that the identification scheme proposed in this paper can more accurately identify asset fingerprints.
The new-type power system has relatively low inertia due to the substantial replacement of synchronous generators (SGs) by converter-interfaced generators (CIGs). Low inertia may result in faster frequency dynamics and threaten the frequency stability of the new-type power system. This paper investigates the inertia response characteristic of typical devices in the new-type power system. By the analogy of the mathematical form of SG inertia, the inertia of asynchronous devices, such as asynchronous motors and CIGs with virtual synchronous generator (VSG) control, can be obtained. The analysis is significant for evaluating of inertia resources in the new-type power system.
For the indirect matrix converter topology composed of bidirectional switching matrix rectifier and traditional voltage source Four-bridge inverter, this paper divides common-mode voltage generated by the indirect matrix converter into inverter stage common-mode voltage and rectifier stage common-mode voltage. A dual-space vector modulation strategy that can effectively suppress common-mode voltage of Four-bridge matrix converter was proposed. Reasonable selection of zero vector of rectifier stage can reduce the effective value of common-mode voltage without increasing switching loss and affecting the utilization rate of DC voltage. Two nonzero vectors with equal amplitude and opposite direction near the output reference voltage vector are selected for zero vector substitution in the inverter stage. The indirect Four-bridge matrix converter was simulated by MATLAB / Simulink. The results show that the improved modulation strategy can not only overcome the negative impact of load imbalance, but also effectively reduce the amplitude and effective value of common-mode voltage without changing the utilization rate of intermediate DC voltage.
In order to improve the accuracy of ultra-short-term wind power prediction, an improved error decoupling approach for wind power prediction result analysis is proposed, with the dependencies of data-driven wind power prediction results on the input data fully considered to make the analysis more reasonable. First, this approach decomposes the prediction process into three key phases, i.e., numerical weather forecast (NWP), wind-power conversion model construction, and prediction result correction. In this respect, the wind prediction errors are assumed to come from three parts, including NWP errors, wind-power conversion modeling errors, and prediction result correction errors. Then, NWP conditions and actual meteorological conditions are separately taken as the inputs of the wind-power conversion model to infer the NWP errors. Further, the information on wind farm operating schedules is exploited to analyze the errors of prediction result correction. Based on these analyses, a group of error decoupling equations are established, whereby the errors in each phase are efficiently estimated by solving the error decoupling equations without iteration. The test results with actual wind farm data show that this approach can more reasonably decouple and estimate wind power prediction errors than existing alternatives.
