Given the increasing uncertainties in power supply and load, this paper proposes the concept of power source and grid coordination uniformity planning.In this approach, the standard deviation of the transmission line load rate is considered as the uniformity evaluation index for power source and grid planning.A multi-stage and multi-objective optimization model of the power source and grid expansion planning is established to minimize the comprehensive cost of the entire planning cycle.In this study, the improved particle swarm optimization algorithm and genetic algorithm are combined to solve the model, thus improving the efficiency and accuracy of the solution.The analysis of a simple IEEE Garver's 6-node system shows that the model and solution method are effective and feasible.Moreover, they are suitable for the coordinated planning of the power source and grid under a diversified nature of power supply and load.

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Recently, power electronic transformers (PETs) have received widespread attention owing to their flexible networking, diverse operating modes, and abundant control objects.In this study, we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs, considering the topology,power balance, loss, and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation, this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization, which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.

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The small-current grounding fault in distribution network is hard to be located because of its weak fault features.To accurately locate the faults, the transient process is analyzed in this paper.Through the study we take that the main resonant frequency and its corresponding component is related to the fault distance.Based on this, a fault location method based on double-end wavelet energy ratio at the scale corresponding to the main resonant frequency is proposed.And back propagation neural network (BPNN) is selected to fit the non-linear relationship between the wavelet energy ratio and fault distance.The performance of this proposed method has been verified in different scenarios of a simulation model in PSCAD/EMTDC.

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The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration, such as operating cost reduction, potential cooperation, other competitors' bidding behavior, and network constraints.The upper model describes the coalition relationship between suppliers, and the lower model represents the independent system operator's optimization without network loss (WNL) or considering network loss (CNL).Then, a novel algorithm, the evolutionary game theory algorithm (EGA) based on a hybrid particle swarm optimization and improved firefly algorithm (HPSOIFA), is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species, with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm (IFA) is combined with a chaotic sequence theory to escape from the local optimum.In addition, the Shapley value is applied to the profit distribution of power suppliers' cooperation.The simulation,adopting the standard IEEE-30 bus system, demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.

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As renewable energy resources increasingly penetrate the electric grid, the inertia capability of power systems has become a developmental bottleneck.Nevertheless, the importance of primary frequency response (PFR) when making generation-expansion plans has been largely ignored.In this paper, we propose an optimal generation-expansion planning framework for wind and thermal power plants that takes PFR into account.The model is based on the frequency equivalent model.It includes investment, startup/shutdown, and typical operating costs for both thermal and renewable generators.The linearization constraints of PFR are derived theoretically.Case studies based on the modified IEEE 39-bus system demonstrate the efficiency and effectiveness of the proposed method.Compared with methods that ignore PFR,the method proposed in this paper can effectively reduce the cost of the entire planning and operation cycle, improving the accommodation rate of renewable energy.

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The increasing penetration of renewable energy into power grids is reducing the regulation capacity of automatic generation control (AGC).Thus, there is an urgent demand to coordinate AGC units with active equipment such as energy storage.Current dispatch decision-making methods often ignore the intermittent effects of renewable energy.This paper proposes a two-stage robust optimization model in which energy storage is used to compensate for the intermittency of renewable energy for the dispatch of AGC units.This model exploits the rapid adjustment capability of energy storage to compensate for the slow response speed of AGC units, improve the adjustment potential, and respond to the problems of intermittent power generation from renewable energy.A column and constraint generation algorithm is used to solve the model.In an example analysis, the proposed model was more robust than a model that did not consider energy storage at eliminating the effects of intermittency while offering clear improvements in economy and efficiency.

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

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In this study, to develop a benefit-allocation model, in-depth analysis of a distributed photovoltaic-powergeneration carport and energy-storage charging-pile project was performed;the model was developed using Shapley integrated-empowerment benefit-distribution method.First, through literature survey and expert interview to identify the risk factors at various stages of the project, a dynamic risk-factor indicator system is developed.Second, to obtain a more meaningful risk-calculation result, the subjective and objective weights are combined, the weights of the risk factors at each stage are determined by the expert scoring method and entropy weight method, and the interest distribution model based on multi-dimensional risk factors is established.Finally, an example is used to verify the rationality of the method for the benefit distribution of the charging-pile project.The results of the example indicate that the limitations of the Shapley method can be reasonably avoided, and the applicability of the model for the benefit distribution of the charging-pile project is verified.

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Africa is embracing new opportunities featured with industrialization, urbanization and regionalization.Based on co-development of ‘Electricity, Mining, Metallurgy, Industry and Trade' and grids interconnection proposed by Global Energy Interconnection Development and Cooperation Organization (GEIDCO), the high-quality hydropower resource of the Congo River can be exploited in large scale under the wide-range interconnected framework of African Energy Interconnection(AEI), forging a new engine for Africa economy.The transmission distance of the Congo River hydropower reaches 6,000 km at its farthest end in North Africa, which brings forth challenges to economics of proposed projects.Under this novel continental energy interconnection scheme in Africa, economics of those projects have not yet been in detail studied.This paper has implemented China's mature engineering experiences and analytical tools of UHVDC project planning into the AEI structure, through exploring the economic behavior of ultra-long distance UHVDC projects in the scope of conductor selection in the Congo River hydropower transmission for the first time, and has provided concerned parties with a technical and analytical results of their economics comparison.This paper has chosen the D.R.Congo-Guinea ±800 kV UHVDC project as a typical example.Its preliminary system planning is introduced and three types of conductor are selected for scheme comparison.Later in this paper, the transmission loss, total investment and equivalent annual cost of the project have been calculated and analyzed.In the final part, sensitivity analysis results of the annual cost to utilization hours,transmission loss, loss tariff and construction cost has been provided.

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A photovoltaic array is environmentally friendly and a source of unlimited energy generation.However, it is presently a costlier energy generation system than other non-renewable energy sources.The main reasons are seasonal variations and continuously changing weather conditions, which affect the amount of solar energy received by the solar panels.In addition, the non-linear characteristics of the voltage and current outputs along with the operating environment temperature and variation in the solar radiation decrease the energy conversion capability of the photovoltaic arrays.To address this problem, the global maxima of the PV arrays can be tracked using a maximum power point tracking algorithm(MPPT) and the operating point of the photovoltaic system can be forced to its optimum value.This technique increases the efficiency of the photovoltaic array and minimizes the cost of the system by reducing the number of solar modules required to obtain the desired power.However, the tracking algorithms are not equally effective in all areas of application.Therefore,selecting the correct MPPT is very critical.This paper presents a detailed review and comparison of the MPPT techniques for photovoltaic systems, with consideration of the following key parameters:photovoltaic array dependence, type of system(analog or digital), need for periodic tuning, convergence speed, complexity of the system, global maxima, implemented capacity, and sensed parameter(s).In addition, based on real meteorological data (irradiance and temperature at a site located in Addis Ababa, Ethiopia), a simulation is performed to evaluate the performance of tracking algorithms suitable for the application being studied.Finally, the study clearly validates the considerable energy saving achieved by using these algorithms.

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