Distribution System Expansion Planning Research Articles

A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

A novel squirrel-cat optimization based optimal expansion planning for distribution system

AimIn recent years, renewable distributed generation (DG) has grown to deliver sustainable electricity with minimal environmental impact. However, renewable DG poses new provocation in the distribution system expansion planning problem (DSEP). To address those problems, this paper suggests a new mathematical model for distribution system expansion plans using a novel hybrid optimization strategy. MethodThe optimal expansion plan for the distribution system is achieved using the hybrid optimization model, named Squirrel Search Insisted Cat Swarm Optimization (SSI-CS) algorithm. The proposed hybrid optimization algorithm is developed with the incorporation of the characteristics features of Squirrel search optimization (SSA) algorithm and Cat Swarm Optimization (CSO) Algorithm to optimize the solar capacity, wind capacity and biomass capacity. This combination aims to strike a balance between global and local optimization, ultimately leading to better cost-effective results. The Distribution systems variables like DG type, size/capacity, location, real power, reactive power, and the solar and wind capacity during load demand uncertainty act as the input to the proposed hybrid optimization algorithm. The main objective of attaining minimal cost for the expansion plan of the distribution system is checked, and the cycle is repeated until obtaining the optimal solution (minimum cost). ResultThe experimental analysis using an IEEE-33 bus system with 5 system states is executed in MATLAB/Simulink. The suggested SSI-CS model attained a minimal operational cost of 7433.4 which better than GWO, SSA and CSO. ConclusionHence, the proposed SSI-CS shows promise as an efficient and effective approach for distribution system expansion planning.

Merchant EV charging station expansion planning

The significant expansion of Electric Vehicles (EV) creates an uncertain load demand that may pose many challenges to conventional planning methods, especially at the distribution network level. The development of EV technology requires charging infrastructure, which must be considered in the Distribution System Expansion Planning (DSEP). Due to the presence of EV and the load they impose on the distribution system, the planning of EV charging stations is done simultaneously with the DSEP so that the system operator can properly consider the technical constraints of the network and operational constraints. This paper presents a bi-level model for EV charging station expansion planning. The main purpose of the first level problem is expansion planning of charging stations and distribution system. But the charging stations are mostly built by a merchant investor, and they do so when it is profitable and has a reasonable risk. Therefore, the merchant investor should be encouraged to build a charging station in the planned areas. For this reason, in the second level problem, the profitability of the charging station and its risk are examined. To solve this problem, genetic algorithm (GA) has been used. Finally, to test the proposed model, the simulation was performed on a modified 69-bus IEEE network.

Efficient expansion planning of modern multi-energy distribution networks with electric vehicle charging stations: A stochastic MILP model

This paper presents a stochastic Mixed-Integer Linear Programming (MILP) model developed to address the collaborative expansion planning of modern multi-energy distribution networks, taking into account the integration of Electric Vehicle Charging Stations (EVCSs) in the presence of uncertainties. The model is comprehensively formulated to encompass Distributed Generation (DG) resources, Electric Vehicles (EVs), and Capacitor Banks (CBs), alongside traditional expansion options like substations and circuit construction/reinforcement. The essence of this Distribution System Expansion Planning (DSEP) problem formulation lies in its stochastic approach, featuring chance constraints to effectively manage and address uncertainties. The central objective of the model is to minimize the total investment and operational expenditures associated with the DSEP problem over a designated planning horizon. The rigorous application of optimization techniques ensures the convergence of the MILP model, thereby delivering an efficient and reliable solution. The model's effectiveness is initially assessed using an 18-bus system, followed by comprehensive testing on a larger 123- bus system to gauge its robustness. Thorough performance evaluations are conducted across various case studies, encompassing both the 18- bus and 123- bus systems, culminating in the validation of the model's resilience, scalability, and replicability. These case studies encompass deterministic and stochastic methodologies across six distinct scenarios. The numerical outcomes conclusively demonstrate that the DSEP solution, when utilizing the stochastic approach without considering EVCSs, is associated with elevated costs. However, with a strategic allocation strategy for EVCSs, noteworthy reductions in investment expenditures related to substations, lines, and overall costs are notably achieved. The incorporation of uncertainty management within the DSEP problem underscores the flexibility and adaptability intrinsic to the proposed stochastic MILP model. These attributes render it ideally suited to effectively orchestrating the expansion of multi-energy distribution networks in the presence of EVCSs, thereby enhancing its utility and relevance in addressing modern energy distribution challenges.

A Risk-Based Planning Approach for Sustainable Distribution Systems Considering EV Charging Stations and Carbon Taxes

Adopting distributed energy resources (DERs) is the key to a low-carbon future in electrical distribution systems (EDS). However, integrating DERs increases the uncertainties in the distribution system expansion planning (DSEP). Thus, the long-term DSEP faces a planning risk brought by the uncertainty of demand, electric vehicle (EV) demand, renewable production, and energy prices. Therefore, this work proposes a novel model for the multi-period planning of EDSs and DERs considering conditional value at risk (CVaR) to manage fluctuations in generation cost and carbon emissions. The proposed mathematical model aims to minimize the net present cost related to investment, operation, and risk. Unlike previous approaches, uncertain behavior of demand growth per planning period is addressed, and the risk is evaluated from two perspectives: planning costs and carbon taxes. Investments in substations, lines, renewable distributed generation, EV charging stations, and energy storage systems are considered. The uncertainties associated with the variability of renewable generation and demand are modeled through a set of scenarios. Finally, the model was evaluated using the 24 and 54-bus EDS. Thus, the proposal is a flexible tool that can be used for different purposes (e.g., carbon taxes, budget limits).

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

A tri‐level approach for coordinated transmission and distribution system expansion planning considering deployment of energy hubs

This paper presents a tri-level stochastic coordinated transmission and distribution system expansion (CTDS) planning considering the deployment of energy hubs (EHs) across the distribution systems (DSs). The first level of the proposed optimization approach deals with transmission system expansion planning (TEP), while the second level develops distribution system expansion planning (DEP). Market clearing is done in the third level to update the locational marginal prices (LMPs) for different distribution system operators (DSOs). The EHs include photovoltaic cells (PVs), wind turbines (WTs), combined heating and power units (CHPs), boilers, and electrical and absorption chillers. The proposed tri-level approach is solved with an iterative algorithm based on the diagonalization approach and tested on the modified IEEE 30-bus transmission systems (TS) with several DSOs. Each DSO includes 18-bus electrical, heating, and cooling energy systems. The numerical analysis shows that the total cost of the transmission system operator (TSO) is not changed with implementing the uncoordinated approach. However, the total cost of the whole system is reduced by 6.54% with accommodation of the EHs in the uncoordinated approach. Moreover, the total cost of the TSO as well as the whole system are reduced by 41.11% and 16.96%, respectively, with the proposed (CTDS) planning.

A Framework for Reliability Assessment in Expansion Planning of Power Distribution Systems

This article proposes a framework that uses analytical assessment of reliability to guide the expansion planning of power distribution systems (PDS) considering reliability criteria. The framework allows the estimation of reliability indices with and without the execution of expansion projects, thus supporting the decision-making process on investments in expansion projects. In the analytical assessment of reliability, failure rates of zones and restoration times are calculated from past data of interruptions in the primary distribution network. In addition, the estimated reliability indices are adjusted to historical values through failure rates proportionate to the length of each zone. To test and validate the proposed framework, it was applied to the distribution network at bus 5 of the Roy Billinton Test System (RBTS) and also to a real distribution feeder located in Brazil. The results indicated that the proposed framework can help define the most attractive investments leading to improvements in reliability indices and reduction in unsupplied energy. The estimation of reliability indices and energy not supplied, considered the following expansion alternatives: (i) the installation of normally-closed sectionalizing switches, (ii) the installation of normally-open switches with interconnection to adjacent feeders, (iii) the automation of switches, and (iv) the reconductoring of zones of the primary distribution network. Nevertheless, the proposed framework allows the inclusion of other expansion alternatives. Finally, the proposed framework proved to be handy and useful for real-life applications.

Robust Distribution System Expansion Planning Incorporating Thermostatically-Controlled-Load Demand Response Resource

Smart meter data provides rich information on customers’ energy consumption behaviors, which can be a valuable resource for evaluating customers’ demand response (DR) potential and thus can inform the distribution system expansion planning (DSEP). This paper presents a novel DSEP framework incorporating a data-driven model for a particular type of incentive-based DR called the thermostatically-controlled-load DR. The heterogeneity in individual customers’ DR potential is considered in the DSEP by leveraging the healing, ventilation, and air conditioning (HVAC) load information extracted from smart meter data. The relationship between the DR incentive and the customer participation rate is also considered so that incentives can be optimally designed for customers with different DR potentials in a differentiated way. The overall DSEP problem is formulated into a robust optimization framework to address the uncertainties from the load demands, renewable energy generations, and DR resources. Case studies show that the proposed DSEP model can substantially reduce the total expansion cost over conventional planning paradigms, demonstrating the positive role of the proposed data-driven DR model in DSEP.

A Specialized Long-Term Distribution System Expansion Planning Method With the Integration of Distributed Energy Resources

The electrical distribution system (EDS) has undergone major changes in the last decade due to the increasing integration of distributed generation (DG), particularly renewable energy DG. Since renewable energy resources have uncertain generation, energy storage systems (ESSs) in the EDS can reduce the impact of those uncertainties. Besides, electric vehicles (EVs) have been increasing in recent years leveraged by environmental concerns, bringing new challenges to the operation and planning of the EDS. In this context, new approaches for the distribution system expansion planning (DSEP) problem should consider the distributed energy resources (DG units, ESSs, and EVs) and address environmental impacts. This paper proposes a mixed-integer linear programming model for the DSEP problem considering DG units, ESSs, and EV charging stations, thus incorporating the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. In contrast to other approaches, the proposed model includes the simultaneous optimization of investments in substations, circuits, and distributed energy resources, including environmental aspects (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emissions). The optimization method was developed in the modeling language AMPL and solved via CPLEX. Tests carried out with a 24-node system illustrate its effectiveness as a valuable tool that can assist EDS planners in the integration of distributed energy resources.

Optimum Distribution System Expansion Planning Incorporating DG Based on N-1 Criterion for Sustainable System

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

An integrated multicriteria decision‐making approach for distribution system expansion planning

The project selection problem in an electric distribution utility, which is part of the network expansion planning task, requires a formal process of prioritizing some projects since the budget is limited. A complete solution to this problem, involving optimization and a decision-making model, is still a few explored in the literature. We proposed an integrated multicriteria decision-making methodology, including optimization and solutions ranking. The optimization module finds a set of Pareto-optimal portfolios, using the multi-objective genetic algorithm nondominated sorting genetic algorithm-II to select and schedule the reinforcement projects in a multistage planning horizon. The proposed decision support module ranks the project portfolios based on Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Simple Multi-Attribute Rating Technique (SMART), considering the decision-maker profile embedded into the model by the Rank-Order Centroid (ROC) and Analytic Hierarchy Process (AHP) criteria weights. This paper tackles this methodology's theoretical foundations, taking into account some essential attributes, such as power quality and operational performance indexes, the number of consumers, and projects' financial impacts. We presented realistic case studies to show how portfolios are composed, not only when all nine attributes of projects are included in the optimization module, but also when the criteria weights are changed in the decision support module. This paper's main contribution lies in proposing a unique model to find optimal expansion plans and rank these project portfolios, considering the decision-maker profile adherent to its goals. About the decision-making methods applied in this problem, we infer that the TOPSIS performance overcomes SMART in all analyzed problems and the ROC and AHP weights indicate the same alternative in most of the analyzed cases.

Investment & generation costs vs CO2 emissions in the distribution system expansion planning: A multi-objective stochastic programming approach

Currently there is a great concern about climate change and its mitigation is one of the main reasons to encourage the development of more sustainable energy systems. Advanced methods are needed to support the planning process in which not just economic criteria are considered but also environmental issues such CO2 emissions related to energy generation. Hence, renewable distributed generation (DG) has been increasing in the last years to provide sustainable energy with low environmental impacts. Nevertheless, renewable DG introduces new challenges in the distribution system expansion planning problem (DSEP) due to its uncertain nature. To deal with those issues, this paper proposes a multi-objective approach based on Stochastic Programming for the DSEP, which addresses the minimization of two conflicting objectives: investment & generation costs and CO2 emissions. The uncertainties related to wind, irradiation, and demand are modeled through representative scenarios under a mixed-integer linear programming formulation. Multi-period investments on substations, circuits, and DG allocation are considered to maintain the feasible operation. The multi-objective formulation is solved using off-the-shelf commercial software and the well-established ε-constraint method. Tests in a 54-node distribution system show that robust expansion plans considering CO2 emissions result in larger penetration of renewable resources; the found set of Pareto solutions represents the trade-off between cost and emission objectives that can be used by the expansion-planner to accomplish specific needs (e.g., budget limitations, emissions reduction target, or environmental constraints).

Flexible expansion planning of distribution system integrating multiple renewable energy sources: An approximate dynamic programming approach

Distribution system expansion planning is a long-term process with sequential multistage uncertain factors, so flexible planning methods are required to deal with the various potential investment risks. This paper presents an approximate dynamic programming based flexible distribution system expansion planning model, in which the long-term system load growth uncertainty and short-term power fluctuation are both considered. A flexible investment strategy based on Markov decision process is developed, in which the planning decisions are made sequentially with consideration of both the current costs and future variable costs under long-term load growth uncertainty. This problem is formulated as a large-scale multistage stochastic programming model, which is intractable due to the “curse of dimensionality”. Thus, an approximate dynamic programming approach is used to decompose the original multistage optimization problem into sequential subproblems that can be easily solved. Case studies are carried out on a 24-node distribution system with three planning stages and a 54-node distribution system with five planning stages. Numerical results validate the feasibility and benefits of the proposed planning approach in obtaining a flexible expansion scheme, which can significantly reduce the investment risk and configure renewable energy equipment more reasonably.

Optimal expansion planning of distribution system using grid-based multi-objective harmony search algorithm

To meet the future power demand, distribution systems (DSs) need to be optimally planned. In this paper, an optimal expansion planning approach of DS is suggested. The capability of the existing DS is evaluated and load forecasting for next ten years is performed. Further, the load flow analysis by using backward-forward sweep algorithm is performed. Results show that existing networks would not be able to meet the future demand and due to this, voltage drop (VD) and power losses might be increased. Therefore, DS expansion planning is carried out considering future demand growth and distributed generation (DG) placement using grid dependent multi-objective harmony search algorithm (GrMHSA). The implementation of GrMHSA optimization technique reduces the total losses as well as VD for the targeted year. A practical Debre Markos (D/M) distribution network of Ethiopia is used for illustrating the superiority of the proposed technique and verifies its effectiveness.

Graph-Based Second-Order Cone Programming Model for Resilient Feeder Routing Using GIS Data

One important task in power distribution system expansion planning is feeder routing (FR), which is to determine the optimal route from a medium voltage substation to load points and the optimal size of the conductors to be installed. This paper proposes a novel graph-based model for the resilient feeder routing problem using geographical information system (GIS). We show that incorporating GIS data enhances the FR solution optimality. Moreover, by introducing a representing graph, radiality of the planned network is guaranteed. A second-order cone programming (SOCP) model is used to model power flows through feeders. Since the representing graph ensures radiality, the SOCP model is exact. The uncertainty of rooftop solar generations and demand forecasting errors are taken into account, and a stochastic programming-based solution algorithm is developed. The proposed model represents practical aspects such as economic objectives (installation cost, power losses, resiliency), technical constraints (voltage limits, radiality constraint, reliability), and geographical constraints. The efficiency of the algorithm is demonstrated using three case studies: a small test system, a realistic one, and a synthetic large test system.

Distribution System Operation and Expansion Planning using Network Reconfiguration

This paper presents an algorithm for network reconfiguration which could help in the operation and expansion planning of three-phase distribution systems. Considering the fact that power losses lead to decrease utility benefits, and the three-phase unbalanced voltage can damage electrical machines, the proposed algorithm is presented as a multi-objective optimization problem based on Evolutionary Algorithms where the power losses and three-phase unbalanced voltage are simultaneously minimized. In order to validate the efficiency of the proposed reconfiguration algorithm, a large-scale radial electric distribution system, under maximum load conditions, is considered for operation planning. In addition, different scenarios are compared to test the algorithm as an expansion planning tool. The proposed algorithm maximizes the utility profits and improves system performance reducing power loss and unbalanced voltage.

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

This paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic,to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD).The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the k-means. Since the resulting stochastic MICPis a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium- or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182-node distribution system.The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.

Two-stage robust optimization for expansion planning of active distribution systems coupled with urban transportation networks

Widespread utilization of electric vehicles has incurred more uncertainties and led to profound interdependencies across the power grid and transportation network. This creates an urgent demand for uncertainty modelling tools. Under this context, this paper proposes a two-stage robust optimization model for expansion planning of coupled active distribution and transportation. This model identifies the best type, location and capacity for roads, distribution lines, distributed generations, static VAR compensators, on-load tap changers, energy storage systems and charging facilities. From a system-level perspective, the steady-state pattern of the traffic flow in a transportation network is characterized by the Wardrop user equilibrium. In the active distribution system, the conic relaxation-based branch flow equations are employed, and the operating status of related devices are formulated by the linearized convex constraints. Considering the interdependency between two coupled systems, a deterministic expansion planning model is first established. Furthermore, with multiple uncertainties considered, a two-stage robust optimization model is proposed to optimize the investment and operation strategies coordinately, and thus a robust optimal solution is achieved. A two-level algorithm based on a combination of the column-and-constraint generation and outer approximation technique is developed for solving the two-stage model. The results demonstrate that the robust approach can effectively reduce the operation cost of coupled systems under the worst-case scenarios. Moreover, a comparative analysis indicates that operation costs in both systems increase linearly as the uncertainty level of traffic demand increases, while increasing that of the power load does not show an obvious change in the traffic operation cost.

Comprehensive approach for distribution system planning with uncertainties

This study presents a comprehensive approach for the distribution system expansion planning (DSEP) that considers investment, operation, carbon dioxide emission and reliability costs, as well as uncertainties over load demand and wind-based distributed generation. A restoration strategy is taken into account for obtaining the energy not supplied under the ‘N − 1’ criterion and the corresponding reliability cost over a planning horizon. The problem is modelled as mixed-integer non-linear programming by using the artificial immune system algorithm. Also, two methods to represent uncertainties are applied and compared: an interval technique through an interval power flow and a scenario-based approach. Network constraints are considered, as the limits of current, voltage and power from substations, as well as the obtaining of radial and connected topology. The novelty of the proposed interval DSEP consists of handling the uncertainties over operation in an efficient manner through a single step, instead of the several deterministic evaluations of the scenario-based approach. Numerical results are presented for well known test systems, which show the potentials of the proposed approach.