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Power Planning Methods Considering Boundary Uncertainties
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The proposal of “double carbon” goal increases the pressure of power structure transformation. This paper sets up two scenarios according to the timing progress of realizing the “double carbon” goal and explores the transformation planning schemes of China’s power structure. The conclusions are as follows: (1) Technological progress and policy support will greatly reduce the levelized cost of electricity (LCOE) of onshore wind power, offshore wind power, photovoltaic power, and photothermal power. The rapid rise in carbon price will lead to the LCOE of coal power in 2060 rising to 2 CNY/kWh. (2) The power consumption of the whole society in the baseline scenario can reach 17,000 TWh in 2060. In the acceleration scenario, this value may triple that in 2020 to 21,550 TWh. (3) The acceleration scenario will pay more newly added power costs and coal power stranded scale than the baseline scenario but can achieve carbon peak and negative emissions earlier. (4) More attention should be paid to the flexible level of power system, improve the allocation proportion and requirements of new energy storage on the power supply side, help the steady exit of coal power, and ensure the safety of low-carbon transformation of power structure.
To solve the problem of large carbon emissions in rural area, this paper conducts field research in rural areas of Shandong and embarks on supply-demand bilateral energy structure optimization which combines local resource endowments, the latest rural energy application technologies and energy consumption preference. The study uses long-range energy alternatives planning (LEAP) model to analyze the trend of energy consumption and CO2 emission from 2020 to 2050 under four different scenarios. The results of Tanzhuang show that residents in rural areas still rely on traditional biomass for energy consumption, especially for winter heating. Energy structure optimization has a significant impact on energy saving and emission reduction. Energy consumption and generation are projected steadily up to 2050 under all scenarios while the carbon emissions show a downward trend with a rapid decline from 2021 to 2030 and a relatively stable state for the next 20 years. Combining policy advancement and marginal carbon abatement cost (MACC) of different energy technologies, energy structure optimization-low cost (ESO-LOW) scenario shows a higher emission reduction benefits than the others. Six other villages in Shandong are analyzed in order to demonstrate the effectiveness of supply-demand bilateral energy structure optimization, sensitivity results show that the optimization is significant to the carbon neutrality target and carbon emission reduction.
With the large‐scale integration of renewable energy into power systems, the strong correlation of wind power output as an important feature will inevitably bring huge impact on power grid planning and operation. However, due to the proliferation of planning and operation scenarios, the traditional power grid planning method can no longer guarantee the operation flexibility and stability under the fluctuation of new energy. In this work, a bi‐level transmission and generation expansion planning (TGEP) model considering the correlation of wind power based on mixed integer linear programming (MILP) is proposed. Firstly, a variable‐structure point diagnosis method is proposed to improve the fitting accuracy of the correlation. Secondly, a scenario reduction method considering the correlation, joint probability distribution and geometric characteristics is proposed, which effectively provides a more matching operating scenarios basis for planning. Thirdly, a multi‐scenario bi‐level TGEP model is proposed. The upper‐level investment decision model aims at the economical goal, while the lower‐level multi‐scenario coordinated optimization operation model aims to minimize the weighted operation index. The impact of the correlation on the results of TGEP is examined and verified in case study.
Integrated energy system (IES) has the advantages of improving energy efficiency, optimizing energy structure and reducing pollutant emissions, so it has become one of the most effective measures to solve the energy and environmental problems. To solve the planning problem of regional integrated energy system (RIES) without a given system structure, a zoning planning method (ZPM) based on energy hub (EH) model and the concept of directed acyclic graph is presented. That is, by analyzing the energy coupling relationship of EH, the EH zoning model based on equipment type is obtained, and the RIES system structure planning model is proposed. This method not only solves the problem that the planning results are influenced by the unfixed number of system structure planning model layers, but also can jointly plan the system structure and equipment configuration of RIES and improve computational efficiency. An illustrative example of RIES planning is given to make comparative analysis of the planning schemes obtained by hierarchical planning method (HPM), three-tier planning method (TPM) based on the equipment type and ZPM. The results show that ZPM can avoid the influence of the unfixed number of system structure planning model layers on the planning results, and can obtain more economical planning schemes and improve the solution speed of the planning model.
With the proliferation of renewable energy resources and amplifying load demand, the capacity expansion planning problem becomes more challenging, primarily due to uncertainties. This paper employs adaptive robust optimization to address the expansion planning problem for transmission line networks and microgrid-based generation units. The key objective is to determine optimal expansion planning decisions that minimize the net system planning cost by anticipating the worst possible realization of long- and short-term uncertainties over the planning horizon from the perspective of a central planner. To model long-term uncertainties associated with future stochastic power generation capacity and power consumption, a cardinality-constraint uncertainty set is formulated. Short-term uncertainties in stochastic power capacity and electricity demand are represented by a set of representative hourly operating conditions. The proposed formulation is structured as a three-level mixed-integer optimization problem under uncertainty, which is subsequently recast into a bi-level optimization problem by amalgamating the second-level objective function with the dual of the third-level optimization problem. This resulting formulation is effectively solved through a tailored column-and-constraint generation algorithm. Numerical analysis has been conducted on the IEEE 24-bus and South Brazilian 46-bus Test systems to demonstrate the proposed model’s capacity and efficacy in expansion planning across diverse operational conditions.
Uneven distribution of loads in three-phase power networks causes voltage unbalances and reduces system’s efficiency. Adding PV generation that is intermittent only makes issues more challenging. Taking into account seasonal changes in both load demand and PV generation, this study presents a new method for the precise placement of PV systems inside unbalanced networks in order to enhance system performance. The most efficient PV hosting is accomplished with the help of a novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) within a multi-objective problem framework. The use of the VAW aggregation strategy may effectively mitigate the limitations associated with the linearization problem with multiple objective functions. This approach is suitable for combining several objectives into a single aggregated objective. Two diverse unbalanced radial distribution systems (URDSs) are considered for the investigation in order to examine and validate the recommended method. Voltage unbalance factor (VUF), voltage security factor (VSF), and active power loss (APL) are three distinctive objectives that are considered to be key contributors to the distribution system performance parameter on an annual basis. The yearly average VSF, VUF, and APL of the Indian 19-bus URDS test network improved by 0.62%, 12.97%, and 38.81% once the PV system was included. Compared to before PV allocation, the modified IEEE 123-bus test network's annual average VSF and APL are improved by 0.081% and 13.42%, respectively. GWO convergence data from the obtained results reveals that it outperforms PSO by reaching the global optimum solution on multiple occasions with regard to test runs.
As a basic link, planning plays an important role in the orderly development of new energy. The traditional new energy power planning has poor accuracy and low service efficiency. In order to solve the above problems, new energy power planning research of a regional photovoltaic power system based on genetic algorithm is proposed. We use the network to connect a number of small power stations together and centrally allocate and improve the breadth and depth of energy utilization. Through the four areas of power data mining, power data processing, power data acquisition and transmission, and power data acquisition gateway, we build a large area photovoltaic power system new energy power planning structure, setting acquisition function, storage function, and display function. Based on the equivalent circuit of power network, combined with the coupling phenomenon elimination function adjustment mechanism, the load parameters of new energy power planning output line are extracted, and the load diagnosis model of new energy power planning output line of large-scale photovoltaic power system is constructed by genetic algorithm. The experimental results show that the planning method has high planning efficiency, good planning accuracy, and stability.
Power grid planning is the basis of power system development, the structure of distribution network in China has changed from single source unidirectional network to multi-source multidirectional complex power network, which further increases the uncertainty and complexity of distribution network operation and planning. From the perspective of distributed energy grid connection, this paper constructs a hierarchical optimization model of distribution network planning under active management mode. The relationship among balanced network loss, line investment, power purchase cost, carbon emission cost and policy subsidies are comprehensively considered. To improve the convergence and effectiveness of the algorithm, NSGA-TS hybrid algorithm is applied for example analysis, which has better global search and local search ability. From the calculation and analysis results of an example, it can be seen that the model is effective in practical analysis, and expands the analysis idea of distribution network planning.