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With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning. [ABSTRACT FROM AUTHOR]

With the increasing scale of wind power installation and grid connection, the interconnection of power grids through tie lines can absorb wind power in a wider range, but the uncertainty of wind power output leads to an increase in the risk of system operation. Conditional value-at-risk (CVaR) was introduced in multi-regional unit commitment to quantify the risk, and a multi-regional unit commitment model considering conditional value at risk was proposed, so as to balance the operation economy and risk loss of multi-regional unit commitment with an interconnected power grid. Besides, the tie line operation mode was modeled in detail to coordinate the economy and flexibility of tie line operation. By introducing auxiliary variables, the tie line operation mode model was transformed into a mixed-integer linear constraint. Finally, the proposed multi-region unit commitment model was transformed into a mixed-integer linear programming form for the solution. With the improved two area system as a simulation example, the results demonstrate that this method can effectively control the tail risk of the system and improve the ability to resist the wind power output uncertainty while ensuring the operation economy of multi-area internet power grid. [ABSTRACT FROM AUTHOR]

Photovoltaic generation is playing a significant role in realizing the target of carbon neutralization. This article proposes a two-stage robust optimization model to assess the maximum hosting capacity of photovoltaic generation in power distribution network (PDN) considering the integration of soft open point (SOP) and electric vehicles (EVs). Convex relaxation method is applied to convert the two-stage robust optimization model to the form of mixed-integer second-order cone programming. A solving strategy based on the column-and-constraint generation algorithm is developed to solve the model, in which linearized difference-of-convex strategy is proposed to deal with the relaxation gap. Case studies are carried out on the modified IEEE 33-bus PDN integrated with SOP and EVs, and the results show the advantages of the proposed model and solving strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-objective distributed generation planning methodology in distribution network considering correlations among uncertainties, i.e., wind speed, light intensity and load demand. First, under the framework of chance constrained programming, a multi-objective distributed generation planning model with the objective functions of minimizing both the annual total cost and the risk is established. The constraints of the model contain not only the restrictions of distributed generation investment and various electrical limitations, but also the restrictions of correlations among uncertainties. Second, an efficient solving strategy is employed to solve the planning model, in which the correlation-handled probabilistic power flow is used to deal with the correlated uncertainties, and non-dominated sorting genetic algorithm II is applied to achieve the Pareto optimal set of the model. Last, case studies are carried out on two test distribution networks, and the results show that a balance between the economy and the security can be achieved by non-dominated sorting genetic algorithm II. The case studies also verify that the correlations among uncertainties can influence the multi-objective distributed generation planning results, and the stronger the correlation is, the bigger the influence will be. [ABSTRACT FROM AUTHOR]

To describe the impact of uncertainties, such as fluctuation of bus loads and intermittent behavior of renewable generations, on the available load supply capability (ALSC) of distribution system accurately and comprehensively, this paper defines a series of meaningful indices for the probabilistic evaluation of ALSC. An efficient simulation method, Latin hypercube sampling-based Monte Carlo simulation (LHS-MCS), combined with step-varied repeated power flow method is proposed to compute the defined indices. Compared with simple random sampling-based Monte Carlo simulation (SRS-MCS), LHS-MCS is found to be more suitable for the probabilistic evaluation of ALSC. It can achieve more accurate and stable ALSC indices under relatively small sample sizes. The calculation speed of LHS-MCS is comparable with that of SRS-MCS under the same sample sizes, and the required CPU time of LHS-MCS is far less than SRS-MCS under the same calculation accuracy. Case studies carried out on the modified Baran & Wu 33-bus and the modified IEEE 123-bus distribution systems verify the feasibility of the defined indices and high performance of the proposed method. [ABSTRACT FROM PUBLISHER]

This paper presents a novel method of power network planning with uncertainty. The unascertained number (UN) method can take the uncertainty of the nodal information with varying degrees of precision into consideration. A new model is defined and a practical case study is presented using the simplified model of the UN method. [Copyright &y& Elsevier]

Summary: High penetration of volatile renewable energy produces uncertainties in power system and poses severe challenges to transmission network expansion planning (TNEP). Usually, only one type of mathematical model of different uncertainties was considered in every single TNEP method. However, in the process of TNEP, different uncertainties may show different mathematical features and need to be represented by various types of mathematical models. Moreover, transmission network is expanded in stages. The dynamic characteristic should be considered. Aiming at these issues, a multistage fuzzy‐robust TNEP model taking into account interval model of renewable power and fuzzy model of predicted load is proposed based on the expanded fuzzy chance constrained programming. In accordance with the feature of the established planning model, the model is transferred to a multistage robust TNEP model considering interval uncertainty model of renewable power and predicted load. Thus, the calculation burden for solving the planning model decreases. A hybrid solving strategy is developed to solve the model, in which the particle swarm optimization (PSO) algorithm is to determine number of new lines in each corridor and the improved branch‐and‐bound (IBB) algorithm is used to check the security of the planning schemes. The analyses on the modified IEEE RTS 24‐bus system and a 231‐bus system verify the effectiveness and adaptability of the proposed method. [ABSTRACT FROM AUTHOR]