Distribution System Expansion Research Articles

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.

An AQ-SCS Optimization Assisted Optimal Expansion Planning with Fixed and Switchable Capacitors for Distribution System

Distribution System has given renewable-based DGs like Solar PV and Wind turbines a lot of attention because of growing worries about global warming and the depletion of fossil fuels. This work proposes a unique hybridized optimization technique for a distribution system expansion plan utilizing an innovative theoretical framework. Two stages are used to tackle the issue with the distribution system expansion plan: master optimization and sub-optimization for every state of the system. The developed Aquila based Sand Cat Swarm (Aq-SCS) optimization method considers the distribution system factors, like DG type, size/capacity, location, PQ power, fixed capacitor, switchable capacitor, and the SPV/wind capacity with load demand uncertainties. The sub-algorithm is employed in conjunction with the DG allocation strategy produced by the master algorithm to deduce the state-dependent operating tactics for each individual DG unit with respect to active and reactive power. The suggested Aq-SCS optimization approach is created by combining the properties of the SCSO with the AOA models. The main objective of achieving the minimal possible cost for the DSEP is verified, and the cycle continues unless the best outcome (least cost) is reached. The analysis of the suggested approach is carried out in 4 cases, such as (i) Without DG and capacitor (ii) With capacitor and no DG (iii) With DG and no capacitor (iv) With both capacitor and DG. The analysis is made in IEEE 33 bus system. The Aq-SCS approach outperforms the conservative approaches SSI-CS, WHO, AQO, and SCSO, according to the analysis conducted in MATLAB/Simulink utilizing an IEEE-33 bus system with five system states.

Various Feature-Based Series Direct Current Arc Fault Detection Methods using Intelligence Learning Models and Diverse Domain Exclusion Techniques

The expansion of DC electrical distribution systems necessitates advancements in detecting and mitigating DC arc events, a significant contributor to fire accidents in low-voltage DC distribution systems. Detecting DC arc faults poses considerable challenges, making them a major safety concern in DC power lines. Conventional approaches mainly rely on arc current, which can vary during normal operation, potentially leading to false alarms. Moreover, these methods often require manual adjustment of detection thresholds for different systems, introducing the risk of malfunction. This study proposes an advanced arc fault recognition procedure that extracts and utilizes various key features for the DC arc detection. This work investigated new various features, which are the square average, the average, the median, the rms, the peak-to-peak, and the variance values, to find out which one can be the most effective features to detect the DC arc failure. The results of this detection process show good evidence for the effectiveness and reliability of the proposed malfunction detecting plan.

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.

Bi-level stackelberg game-based distribution system expansion planning model considering long-term renewable energy contracts

With the deregulation of electricity market in distribution systems, renewable distributed generations (RDG) are being invested in by third-party social capital, such as distributed generations operators (DGOs) and load aggregators (LAs). However, their arbitrary RDG investment and electricity trading behavior can bring great challenges to distribution system planning. In this paper, to reduce distribution system investment, a distribution system expansion planning model based on a bi-level Stackelberg game is proposed for the distribution system operator (DSO) to guide this social capital to make suitable RDG investment. In the proposed model, DSO is the leader, while DGOs and LAs are the followers. In the upper level, the DSO determines the expansion planning scheme including investments in substations and lines, and optimizes the variables provided for followers, such as RDG locations and contract prices. In the lower level, DGOs determine the RDG capacity and electricity trading strategy based on the RDG locations and contract prices, while LAs determine the RDG capacity, demand response and electricity trading strategy based on contract prices. The capacity information of the DRG is sent to the DSO for decision-making on expansion planning. To reduce the cost and risk of multiple agents, two long-term renewable energy contracts are introduced for the electricity trading. Conditional value-at-risk method is used to quantify the RDG investment risk of DGOs and LAs with different risk preferences. The effectiveness of the proposed model and method is verified by studies using the Portugal 54-bus system.

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.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Optimal AC/DC Distribution Systems Expansion Planning From DSO's Perspective Considering Topological Constraints

As the integration of DC distributed resources increases in recent years, hybrid AC/DC topology emerges as a promising alternative in the planning of distribution systems. This paper presents an AC/DC optimal expansion planning of distribution networks from Distribution System Operator's perspective with a special focus on grid architecture through Integer Programming-based topological constraints. The proposed formulation aims to determine the optimal location and sizing of converters and to select lines type (AC or DC) and state (closed or open). The problem is formulated as a mixed integer second-order conic programming model that guarantees global optimality and verifiable exactness by using off-the-shelf solvers. A multi-scenario approach with a Wasserstein distance-based scenario reduction is applied to deal with resource uncertainty. Case studies based on a 13 bus and IEEE 33 bus distribution networks were used to verify the effectiveness of the model. The results show that a hybrid architecture can improve the efficiency of the grid through optimal active power flow and reduce the total network costs.

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).

Development and Validation of a Load Flow Based Scheme for Optimum Placing and Quantifying of Distributed Generation for Alleviation of Congestion in Interconnected Power Systems

The energy supply entities widely adopt distributed generators (DG) to meet the additional power requirement due to scheduled or unscheduled interruptions. The expansion of transmission and distribution systems via the inclusion of loads and generators and the occurrence of line interruptions are significant causes of congestion of transmission lines in interconnected systems. The management and alleviation of congested lines is a primary requirement for a power system network’s reliable and efficient operation. The researchers investigated the potential scope of distributed generation (DG) to alleviate the congested branches in interconnected transmission systems. The development of a reliable scheme to arrive at the best location and size of local generators for alleviating congestion deserves considerable importance. This paper attempted to develop a simple and reliable strategy for the optimum placement and sizing of DGs to be integrated with a transmission line system of DGs for congestion relief in transmission lines by analyzing power flow solutions. This research work considered the 14-bus system of IEEE for the preliminary analysis to identify the parameters employed for assessing the severity of line congestion and the best placement and sizing of DGs for congestion relief. This work analyzed power flows by load flow algorithms using ETAP software in the 14-bus IEEE system for different line outage cases. The analysis of power flow solutions of the 14-bus system of IEEE revealed that the percentage violation of the system can be regarded as an essential parameter to assess the extent of congestion in an interconnected system. A detailed power flow analysis of the system with various capacities of DG integration at several buses in the system revealed the application of two indices, namely the index of severity (SI) and sensitivity factor (SF), for optimum placement with the best capacity of DGs for congestion alleviation in the system. This work proposed a reliable algorithm for the best siting and sizing of DGs for congestion relief by using the identified indices. The proposed methodology is system indices allied load flow-based algorithm. This work produced a fast simulation solution without any mismatch through this developed scheme. The approximations linked with the algorithm were very minute, resulting in comprehensive bests instead of inexact limited bests with less simulation time and more convergence probability and availing the benefits of the mathematical approach. The work investigated the feasibility of the proposed methodology for optimum placing and quantifying DGs for congestion solutions for a practical interconnected bus system in the supply entity of the Kerala grid with many buses. Any transmission system operator can adopt this method in similar connected systems anywhere. The proposed algorithm determined the most severe cases of congestion and the optimum site and size of DGs for managing congested feeders in the grid system. The analysis of the losses in the system for different cases of DG penetration by load flow analysis validated the suitability of the obtained results.

A Two-Layer Approach for Estimating Behind-the-Meter PV Generation Using Smart Meter Data

As the cost of the residential solar system decreases, rooftop photovoltaic (PV) has been widely integrated into distribution systems. Most rooftop PV systems are installed behind-the-meter (BTM), i.e., only the net demand is metered, while the native demand and PV generation are not separately recorded. Under this condition, the PV generation and native demand are invisible to utilities, which brings challenges for optimal distribution system operation and expansion. In this paper, we have come up with a novel two-layer approach to disaggregate the unknown PV generation and native demand from the known hourly net demand data recorded by smart meters: 1) At the aggregate level, the proposed approach separates the aggregate PV generation time series from the aggregate net demand time series for customers with PVs. 2) At the customer level, the separated aggregate-level PV generation is allocated to individual PVs. These two layers leverage the spatial correlations of native demand and PV generation, respectively. One primary advantage of our proposed approach is that it is more independent and practical compared to previous works because it does not require PV array parameters, meteorological data and previously recorded solar power exemplars. This paper has verified our proposed approach using real native demand and PV generation data.

Unbalanced Distribution System Expansion and Energy Storage Planning Under Wildfire Risk

Unbalanced Distribution System Expansion and Energy Storage Planning Under Wildfire Risk

A two-way pre-installation assessment framework for microgrids under power systems expansion planning

Many remote areas remain off the grid and are disconnected from the mainstream power network due to congestion in the existing power networks and expensive expansion plans. In recent studies, microgrid installations appeared as one of the most feasible solutions for power system expansion. This research study develops a two-way (M to G), where the existing microgrid is integrated into the distribution network, and (G to M) where distribution system expansion recommends the installation of a microgrid. The proposed framework is based on three steps. The initial assessment is based on a fuzzy assessment tool utilizing social factors, geographic location, and accessibility followed by economic feasibility analyses. These analyses are based on HOMER software and a power flow study of the microgrid to assess the system’s reliability. The proposed framework is implemented on an islanded microgrid in a village in Pakistan. The acquired results have shown that the proposed framework recommends the existing microgrid expansion in M to G mode. The Net Present Cost (NPC) of the grid-connected system is reduced from $11 million to $0.7 million. The cost of energy is also reduced to $4.42 compared to the $65 of the islanded systems, while the payback period is only 1.2 years compared to that of the islanded microgrid which is 8.6 years.

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.

Security-constrained AC–DC hybrid distribution system expansion planning with high penetration of renewable energy

With the increasing penetration of renewables into the AC–DC hybrid distribution system, it is necessary to develop a security-constrained planning approach to address the uncertainty brought by renewables. Motivated by the urgent need to accommodate high penetration of renewable energy resources in weak grids, this paper presents a novel planning approach for AC–DC hybrid distribution systems considering the N-1 security criterion. Network reconfiguration is incorporated as a method to improve the flexibility of the system under normal and post-contingency conditions. To alleviate the burden of computing all possible N-1 contingencies, a novel solving strategy with a nested loop structure is proposed. In the inner loop, a novel contingency screening method is designed for the fast identification of high-risk contingencies under a given planning solution. In the outer loop, the obtained high-risk contingency set is adopted to refine and filter the security constraints, so that the planning solution can be improved iteratively. The superiority and effectiveness of the proposed planning model and solving strategy are tested on a real-world distribution network in Zhejiang Province, China. Numerical results verify that the proposed planning approach is able to deal with the installation of large-scale renewable energy while ensuring the security of the system.

Expansion Planning of Power Distribution Systems Considering Reliability: A Comprehensive Review

One of the big concerns when planning the expansion of power distribution systems (PDS) is reliability. This is defined as the ability to continuously meet the load demand of consumers in terms of quantity and quality. In a scenario in which consumers increasingly demand high supply quality, including few interruptions and continuity, it becomes essential to consider reliability indices in models used to plan PDS. The inclusion of reliability in optimization models is a challenge, given the need to estimate failure rates for the network and devices. Such failure rates depend on the specific characteristics of a feeder. In this context, this paper discusses the main reliability indices, followed by a comprehensive survey of the methods and models used to solve the optimal expansion planning of PDS considering reliability criteria. Emphasis is also placed on comparing the main features and contributions of each article, aiming to provide a handy resource for researchers. The comparison includes the decision variables and reliability indices considered in each reviewed article, which can be used as a guide to applying the most suitable method according to the requisites of the system. In addition, each paper is classified according to the optimization method, objective type (single or multiobjective), and the number of stages. Finally, we discuss future research trends concerning the inclusion of reliability in PDS expansion planning.

A Tri-Level Planning Approach to Resilient Expansion and Hardening of Coupled Power Distribution and Transportation Systems

Natural disasters which include major storms, floods, tornados, and hurricanes can seriously threaten the resilience of large and coupled infrastructures such as electric power distribution and transportation systems. This paper proposes a planning (i.e., investment + operation) method for enhancing the resilience of coupled power distribution and transportation systems. The proposed investment method includes capacity expansions of power lines, roads, and charging stations, and the hardening of roads and power lines. A tri-level problem is proposed and formulated to accommodate random natural disasters. The proposed model is solved by applying the Benders decomposition and the column-and-constraint generation (C&CG) algorithms. Benders decomposition will decompose the tri-level coupled problem into a single-level master problem and a bi-level subproblem. However, the latter with binary variables is not a convex problem and cannot be converted to the maximization problem. The C&CG algorithm is applied to solve the bi-level subproblem with binary variables. The proposed resilience enhancement algorithm is tested using a coupled power distribution and transportation system with 21 electric buses and 20 roads and numerical results are analyzed to validate the effectiveness of the proposed planning method.

Integration of Renewable Based Distributed Generation for Distribution Network Expansion Planning

Electrical energy is critical to a country’s socioeconomic progress. Distribution system expansion planning addresses the services that must be installed for the distribution networks to meet the expected load need, while also meeting different operational and technical limitations. The incorporation of distributed generation sources (DGs) alters the operating characteristics of modern power systems, resulting in major economic and technical benefits, such as simplified distribution network expansion planning, lower power losses, and improved voltage profile. Thus, in this study, an analytical method is used to design the expansion planning of the Addis North distribution network considering the integration of optimal sizes of distributed generations for the projected demand growths. To evaluate the capability of the existing Addis North distribution network and its capability to supply reliable power considering future expansion, the load demand forecast for the years 2020–2030 is done using the least square method. The performance evaluation of the existing and the upgraded network considering the existing and forecasted load demand for the years 2030 is done using ETAP software. Accordingly, the results revealed that the existing networks cannot meet the existing load demand of the town, with major problems of increased power loss and a reduced voltage profile. To mitigate this problem, the Addis North feeder-1 distribution network is upgraded and for each study case, the balanced and positive sequence load flow analysis was executed and the maximum total real and reactive power losses were found at bus 29. The result shows that the upgraded network of bus 29 was the optimal location of DG and its size was 9.93 MW. After the optimal size of DG was placed at this bus, the real and reactive power losses of the upgraded networks were 0.2939 MW and 0.219 MVAr, respectively. At bus 29 the maximum power losses reduction and voltage profile improvements were found. The active and reactive power losses were minimized by 21.285% and 19.633% respectively and the voltage profiles were improved by 8.78%. Thus, in the predicted year 2030, DG power sources could cover 61.12% of the feeder-1 power requirements.

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.

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

Smart meter data provides rich information on customers&#x2019; energy consumption behaviors, which can be a valuable resource for evaluating customers&#x2019; 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&#x2019; 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.