Active Distribution Network Planning Research Articles

A Multi-objective Optimization Planning Framework for Active Distribution System Via Reinforcement Learning

The effective planning of active distribution networks is crucial for utility companies to make informed decisions regarding investments in distributed generation, reliability assessment, reactive power planning, substation revisions, and feeder repositioning. However, the dynamic nature of the solution space makes it challenging for model-based optimization methods to ensure computational performance in active distribution network planning. To address this issue, this study proposes a planning method that focuses on improving computational performance through the continuous updating of the planning model’s solution space during the reinforcement learning training process. Based on simulations conducted on the IEEE 33-bus test system, the proposed planning strategy successfully enhances computational performance while minimizing investment costs compared to other strategies. With the proposed method, the investment cost and the operation cost are reduced by 32.42% and 23.91%, respectively.

Active distribution network type identification method of high proportion new energy power system based on source-load matching

A large number of distributed new energy and flexible loads are connected to the distribution network, resulting in increasingly complex source load characteristics and supply and demand situations. The analysis of source load characteristics and accurate division of network types are the basis for the accurate planning of active distribution networks. Here, we report an active distribution network type identification method based on source-load matching. Firstly, the typical daily output scenarios of DG are extracted by clustering method, and the generalized load curve model is solved by the optimization algorithm to obtain the source load operation data; Secondly, calculate the source-load matching indicators (including matching performance, matching degree, and matching rate) according to the source load data of each region, and identify the distribution network type according to the range of the index values; Finally, several indicators are introduced to quantify the characteristics of different types of distribution networks. A case study of a power supply grid in Zhejiang Province is used to verify the practicability of the proposed method for active distribution network planning.

Research on urban energy Internet planning based on fuzzy comprehensive method

Abstract With the continuous development of China’s economy, the process of urbanisation and industrialisation has been accelerating, and coupled with the emergence of new energy, China’s urban energy consumption has undergone huge transformation. The urban energy Internet (UEI) is an important part of the country’s ‘carbon peak and carbon neutrality’, and it is also one of the key tasks in the planning, construction and operation of the power grid transformation. The traditional grid-side planning model relies on a single deterministic planning method, and there is less consideration for new influencing factors, which has been difficult to meet the energy demand in the context of the energy Internet. In this paper, research on UEI planning based on the fuzzy synthesis method is proposed. First, the basic architectural model of UEI is constructed. At the same time, the impact of distributed generation, energy storage, demand response, communication and other key technologies on the planning of active distribution networks in the distribution network planning under the UEI is analysed. Then, a fuzzy comprehensive method is proposed to calculate the weight of UEI planning. Finally, the planning and research of UEI prospects from the aspects of equipment form, system planning, operation control, business model and collaborative development are discussed.

A Hierarchical Framework for Day-Ahead Optimal Operation Planning of Active Distribution Networks with Multi-Microgrids

HIGHLIGHTS Multi-microgrids and distributed energy resources in active distribution networks (ADNs). A coordinated hierarchical optimization model. Optimal day-ahead operation planning for ADNs with Multi-Microgrids.

Reliability-Based Expansion Planning Studies of Active Distribution Networks With Multiagents

In this paper, a multi-agent framework is proposed to address the expansion planning problem in a restructured active distribution network. In this framework, the objective and techno-economic constraints of participating agents are addressed in the expansion planning of power network and DER assets as well as the network and DERs optimal operation management. The agents include distributed generator owners and load aggregators which participate along with the distribution network operator (DNO) in the active distribution network planning. The proposed framework is formulated as a bi-level optimization problem with multi-lower levels in which the DNO optimizes the network expansion planning and operation in the upper level where the network reliability is also modeled. In the lower level problems, other participating agents optimize the expansion planning and operation of their assets. The model addresses the optimal operation of resources by clearing the local energy market. The uncertainties in the upper and the lower level problems are effectively addressed using a stochastic adaptive robust optimization approach. The proposed model is implemented on the 54-bus and 138-bus distribution networks and the results demonstrate the effectiveness and scalability of the proposed framework.

Expansion planning of active distribution networks achieving their dispatchability via energy storage systems

This paper presents a combined framework for power distribution network expansion planning (DNEP) and energy storage systems (ESSs) allocation in active distribution networks (ADNs) hosting large amount of photovoltaic (PV) generations and loads. The proposed DNEP ensures the reliable operation of the targeted ADN with the objective of achieving its dispatchability while minimizing grid losses by determining the optimal grid expansion to connect new nodes, the reinforcement of existing lines, and the ESS allocation. The allocated ESSs compensate for the stochastic power flows caused by the stochastic loads and generation, allowing ADNs to follow a pre-defined power schedule at the grid connection point. The grid constraints are modeled by using a modified augmented relaxed optimal power flow (AR-OPF) model that convexifies the classical AC-OPF providing the global optimal and the exact solution of the OPF problem for radial networks. The DNEP problem’s complexity is handled by employing a sequential algorithm where new nodes are added one by one, following the priorities determined by the user. In each stage of the sequential planning, the Benders decomposition algorithm determines the optimal solution for investments and ADN operation iteratively. Moreover, the siting and sizing problems associated with the ESSs and line investment are solved separately to enhance the convergence speed. Simulations are conducted on a real 55-node Swiss ADN hosting sizeable stochastic photovoltaic generation.

A risk‐averse multiyear framework for resilience improvement planning of active distribution networks against hurricanes

This paper proposes a risk-averse strategy for resilience improvement multiyear planning for distribution networks against hurricanes, considering the uncertainty of load demand and wind power generation. For resilience enhancement, DG placement and line hardening are regarded as planning measures, and DG rescheduling, topology reconfiguration, microgrid forming, and priority-based load curtailment are considered as operational measures. The most conservative scenario is considered for modelling the high-impact/low-probability (HILP) event's impacts. Besides, the information-gap decision theory (IGDT) is utilized to acquire a robust solution protected against the different realization of uncertain variables. As the uncertain loads and wind power generation competitively tend to maximize their robust regions for a particular value of the budget of uncertainty, the ε-constraint method is applied to solve the proposed multi-objective IGDT-based resilience-oriented distribution network planning (RODNP) model. Afterwards, fuzzy decision-making is exploited to find the best solution among Pareto optimal solutions. The proposed risk-averse strategy is implemented on IEEE 33-bus test system, and various case studies are analyzed to demonstrate the proficiency of the approach. The results indicate that the proposed risk-averse strategy can optimally find the hardening schemes to achieve a resilient distribution network in a medium-term planning horizon with a yearly limited monetary budget.

A multi-objective bi-level optimization framework for dynamic maintenance planning of active distribution networks in the presence of energy storage systems

Improving the reliability of microgrids as well as satisfying technical and economic constraints are very important challenges for distribution system operators (DSOs), which should be considered in both the long-term and short-term horizons. Therefore, this paper presents a dynamic model for multi-microgrids maintenance planning over a 10-year period, taking into account daily operating conditions. The proposed model is designed as a bi-level problem, in the first level of which the microgrids perform their maintenance planning and report its result to the DSO. Then, in the second level, DSO performs day-ahead scheduling of the entire network with the goals of adhering to microgrids schedule and minimizing system average interruption frequency index (SAIFI). Note that the objective functions of both levels are formulated in the form of two-objective problems through the Epsilon-constraint method. Finally, the proposed model is implemented on a 69-bus distribution network and results demonstrate that maintenance services, despite the increase in planning costs, will lead to a significant reduction in operating costs and will be cost-effective in the long-term horizon. The results also indicate that distribution feeder reconfiguration (DFR), despite a 19.62% increase in maintenance costs, leads to a 15.23% reduction in operating costs. Finally, the results illustrate that coordinated operation of storage systems and demand response (DR) programs reduces operating costs by 5.67%.

Stochastic Second-Order Conic Programming for Optimal Sizing of Distributed Generator Units and Electric Vehicle Charging Stations

The increased penetration of electric vehicles (EVs) and distributed generator (DG) units has led to uncertainty in distribution systems. These uncertainties—which have not been adequately considered in the literature—can entail risks in the optimal sizing of EV charging stations (EVCSs) and DG units in active distribution network planning. This paper proposes a method for obtaining the optimal sizing of DG units and EVCSs (considering uncertainty), to achieve exact power system analysis and ensure EV driver satisfaction. To model uncertainties in optimal sizing planning, this study first generates scenarios for each system asset using a probability distribution that considers the asset characteristics. In this step, the wind-turbine (WT), PV, and EVCS are modeled applying the Weibull, exponential, and kernel density estimation (KDE), and scenarios for each asset are generated using random sampling. Then, the k-means clustering is carried out for scenario reduction and the representative scenario abstract. The probability of occurrence for each representative scenario is assigned depending on the number of observations within each cluster. The representative scenarios for each asset are integrated into the scenario for all assets through the joint probability. The integrated scenarios are applied in the optimization problem for optimal sizing of the system asset framework. The optimal sizing of the system assets problem is proposed (to minimize the line loss and voltage deviation) and formulated via stochastic second-order conic programming, to reflect the uncertainty under an AC power flow; this is a convex problem that can be solved in polynomial time. The proposed method is tested on a modified IEEE 15 bus system, and the simulation is performed with various objective functions. The simulation results demonstrate the effectiveness of the proposed method.

Reinforcement learning for active distribution network planning based on Monte Carlo tree search

Active distribution network planning is of importance for utility companies in terms of distributed generation investment, reliability assessment, optimal reactive power planning, substation evaluation, and feeder reconfiguration. However, it is challenging for current model-based optimization problems to guarantee the performances of active distribution network planning, due to an empirically pre-defined solution space. To overcome this issue, this paper proposes a performance-oriented method for the active distribution network planning. The solution space of the planning model is dynamically updated through using deep neural networks which are trained by the Monte Carlo tree search-based reinforcement learning until the desired performances are satisfied. Simulation results based on the standard IEEE 33-bus test system demonstrate that the proposed method can successfully improve the performances of the active distribution network planning to a desired level at a lower investment cost compared to other cases.

Reliability‐constrained robust expansion planning of active distribution networks

Power distribution systems have become more complex in recent years due to the integration of new technologies. Motivated by these challenges, this paper describes a model to solve the multi-period expansion planning problem of active distribution systems, considering uncertainty, reliability, distributed generation, self-healing, reactive power support, switches, and energy storage devices. The objective is to determine the best installation times and locations of new components, minimizing the total cost, and ensuring desired reliability levels. The approach consists of a three-level decomposition. The first level identifies expansion proposals, the second level finds the worst-case scenario using adaptive robust optimization, and the third level performs a Monte Carlo Simulation to compute reliability indexes. The main contributions are the introduction of a novel reliability sensitivity matrix to improve computational performance and the representation of the hours of the day inside the expansion planning formulation. The proposed method is illustrated using the IEEE 123-bus test system. The analyses show high computational efficiency as compared with similar works. The impacts on the number of expansion components placed in the system and on the total cost are presented and discussed using cases varying the uncertainty budget and not considering some of these components.

Regional active distribution network planning study based on robust optimization

With the increasing installed capacity of new energy generation, the randomness and volatility of new energy generation has seriously affected the stable operation of the grid. And the traditional generation side dispatching method can no longer meet the demand of the grid to maintain stable operation, which greatly affects the consumption of new energy. In order to improve the rate of new energy consumption and economic efficiency, this paper proposed a regional active distribution network economic evaluation index that takes construction costs, operating costs, and new energy subsidies into account. What is more, the paper summarized the physical constraints of each equipment in the regional active distribution network. Combining the economic evaluation index and the physical constraints, the paper raised a regional active distribution network planning and scheduling model based on robust optimization. Finally, the regional active distribution network planning model based on robust optimization is validated in terms of the new energy consumption rate and the economic benefits by optimizing the examples of load-based regional active distribution network.

Coordinated operation and expansion planning for multiple microgrids and active distribution networks under uncertainties

Power systems have incorporated many new technologies in recent years and the introduction of energy storage systems and demand response provides additional challenges for their operation and expansion planning. Current distribution networks are complex active networks where many technologies are included, which must be considered and properly represented in expansion planning models. Thus, this paper proposes a robust model to solve the coordinated operation and expansion planning of active distribution networks with multiple microgrids, distributed energy resources (DERs), demand response, and N-1 generation contingency. The objective is to determine the best expansion planning proposals and the optimal daily operation (24-hour representation) for the DERs under the worst realization of uncertainties, using a contingency-constrained approach. Besides, the load is represented by non-controllable, controllable, and deferrable portions. The model is formulated as a tri-level problem that is solved using a two-stage robust optimization approach. The proposed method is illustrated using a modified version of the IEEE 123-bus test system. The results show that the coordinated operation and expansion planning decreases the total cost of the problem and provides more consistent results for demand response and DERs coordination. Simulations show a reduction of 13.8% in the total cost if the daily operation and demand response are represented, and the inclusion of contingencies provides a reduction of 70% in the EENS (expected energy not served) reliability index for the case study.

Optimization Decision Method based on Nonlinear Analytic Hierarchy Process for Active Distribution Network Planning

Aiming at the investment environment of the distribution network, this paper deeply analyses the investment conditions of the distribution network, introduces the analytic hierarchy process and modern investment theory, establishes the analytic hierarchy of the investment conditions in the distribution network project, and proposes an optimization decision-making method of investment plan in distribution network. This paper also establishes two new concepts: condition score and correlation score, uses the improving analytic hierarchy process to calculate the weights, proposes a priority ranking method for distribution network projects, and gives detailed process. to formulate distribution network investment plans. The application of the proposed method to optimize the distribution network planning scheme shows that the optimization investment scheme given has better investment benefits under the condition of the power supply reliability requirements of the distribution network and investment capital constraints.

Research on Active Distribution Network Planning with Electric Vehicles and Distributed Generation

With the increasing number of Electric Vehicle (EV) and clean energy generation year by year, EV and distributed generation (DG) have become issues that have to be considered in active distribution network planning. Firstly, considering the time series characteristics of DG, the output time series model of DG is established; Secondly, the parking demand and space-time movement model of EV is established, and the Monta Carlo method is used to simulate the space-time distribution of EV charging load in different planning areas; Finally, taking the system investment and annual operation and maintenance cost, voltage index and environmental index as the objective function, and considering the node voltage, node current and DG installation capacity as constraints. The improved particle swarm optimization algorithm is used to solve the planning model, and the access location and capacity of EV charging station and DG are obtained. Taking a distribution network as an example, the rationality and effectiveness of the proposed model and algorithm are verified.

Bi-level Coordinated Planning of Active Distribution Network Considering Demand Response Resources and Severely Restricted Scenarios

Due to the uncertainty and fluctuation of distributed generation (DG) and load, the operation of active distribution network (ADN) is affected by multi-dimension factors which are described by massive operation scenarios. Efficient and accurate screening of severely restricted scenarios (SRSs) has become a new challenge in ADN planning. In this paper, a novel bi-level coordinated planning model which combines the short-time-scale operation problem with the long-time-scale planning problem is proposed. At the upper level, the demand response (DR) resource, an effective non-component planning resource characterized by low capacity price, high energy price, and short contract term, is co-optimized with the configuration of lines and energy storage systems (ESSs) to achieve the economic trade-off between the investment cost and the operation cost under SRSs. At the lower level, with the planning scheme obtained from the upper level, massive operation problems are optimized to minimize the daily operation cost; and the SRSs are provided to the upper level through a shadow-price-based scenario screening method, which simulates the planning information (i.e., the restricted degrees of operation scenarios) feedback process from ADN operators to ADN planners. Case studies on a 62-node distribution system in Jianshan New District, Zhejiang Province, China, illustrate the effectiveness of the proposed bi-level coordinated planning model considering DR resources and SRSs.

Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

Coordinated Investment Planning of Distributed Multi-Type Stochastic Generation and Battery Storage in Active Distribution Networks

This paper presents a scenario-based stochastic active distribution network planning (ADNP) model considering the multi-type distributed generation and battery energy storage (BES). The proposed solution aims to identify the optimal mix, siting, and sizing of wind turbine (WT), photovoltaic (PV), and BES units to maximize the net present value of distribution network operator (DNO) while fully exploiting the BES arbitrage benefit. First, a heuristic moment matching based uncertainty matrix comprising of representative scenarios is generated to effectively capture the stochastic characteristics and correlation among historical WT generation, PV generation, and load demand. Then, the uncertainty matrix is incorporated to formulate the stochastic ADNP problem. The proposed solution minimizes the costs and maximizes the revenues of the DNO. The effectiveness and scalability of the proposed model are evaluated through case studies in the 53-bus and IEEE 123-bus distribution systems. Finally, the performance of the proposed model is compared against the deterministic planning model, and a sensitivity analysis is performed to assess the impact of various planning factors on the proposed solution.

Expansion Planning of Active Distribution Networks With Multiple Distributed Energy Resources and EV Sharing System

The ever-increasing energy demand and high penetration rate of distributed renewable generation brings new challenges to the planning of power distribution networks. This paper proposes an expansion planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems. The operational characteristics of SEV are considered and modeled. The proposed planning model aims to minimize the weighted sum of network investment cost, energy losses, and queue waiting time of SEVs. A stochastic scenario generation method is introduced to address the stochastic feature of SEVs’ driving behaviors. Numerical studies are tested on the systems with 54-node distribution network and 25-node traffic network.

Joint planning of active distribution networks considering renewable power uncertainty

This paper proposes a multi-objective joint planning model for Active Distribution Networks (ADNs). The model determines the location and size of Electric Vehicle Charging Stations (EVCSs), Renewable Energy Sources (RESs), Battery Energy Storage System (BESSs), and distribution network expansion schemes, with the objectives of minimizing the total investment and reliability cost of the distribution network and maximizing the EVCSs’ charging service capability. A scenario-based stochastic modelling approach based on Wasserstein distance metric and K-medoids scenario analysis is developed to model the stochastic nature of renewable generation. A multi-objective optimization algorithm, Multi-Objective Natural Aggregation Algorithm (MONAA), is applied to solve the proposed model. Case studies are conducted on a coupled 54-node distribution system and 25-node traffic system to validate the effectiveness of the proposed method.