Expansion Planning Model Research Articles

Stochastic multistage co-planning of integrated energy systems considering power-to-gas and the cap-and-trade market

This paper develops an expansion co-planning model for integrated power and gas systems where uncertainties in both systems are considered. The power-to-gas technology is modeled and considered as a potential solution for some worldwide societal goals such as decarbonized economy and 100% renewable penetration. Compared with expansion planning models for power systems, the complexity of expansion co-planning models is severely exacerbated by random variables in gas systems. A scenario based decomposition scheme called branch-and-price is presented. The numerical results demonstrate the effectiveness of the proposed method and show the economic value of power-to-gas technology.

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.

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.

Generation expansion planning with high shares of variable renewable energies

Worldwide, the utilization of Renewable Energies (REs) for electricity generation is growing rapidly driven by the increasing fears of fossil fuels depletion, the price volatility of these fuels and the necessity of reducing the Green House Gas (GHG) emissions to preserve the environment. On the other hand, REs especially the Variable Renewable Energies (VREs) like wind and solar power suffer from intermittency in its output generation. This intermittency can introduce severe technical and economic problems for the power systems with high penetration from these energies. This intermittency should be mitigated not only during the system operation phase but also during power system planning phase. For this purpose, the classical power system planning methodologies and models should be upgraded to account for this intermittency in a way to find the optimum solutions to mitigate it. In this regard, this paper will focus on developing a new Generation Expansion Planning (GEP) model to find the optimum mix of dispatchable generation technologies that can allow the integration of VREs into the power system while mitigating the technical and economic impacts of its intermittency. In addition, a number of new concepts related to generation mix flexibility, VREs capacity credit and role of system operating reserve in integrating VREs will be revisited. Then, the developed GEP model will be applied to a case study handling the future expansion scenarios of VREs in the Egyptian grid. Results obtained show that, increasing the share of VREs in the grid will shift the mix of new generation capacities from the least cost and low flexibility options into more expensive and flexible generation options.

Probability-Interval-Based Optimal Planning of Integrated Energy System With Uncertain Wind Power

Owing to a higher energy supply efficiency and operational flexibility, integrated energy system (IES), including the power, heating, and gas systems, will be the primary form of energy supply in the future. However, with the increase of large-scale stochastic wind power integration, the IES planning will face a significant challenge as the traditional power system. Therefore, a probability-interval-based IES planning considering wind power integration is proposed in this article. First, a conditional value-at-risk (CVaR) based probability-interval method is developed to describe the uncertain wind power. Second, beside traditional facilities, electricity storage system is introduced to improve the flexibility of IES. Then, an expansion planning model for IES is established to minimize the total cost including investment, operation, CVaR cost, and unserved energy cost. Moreover, the piecewise linearization method is used to deal with the nonlinear integral terms of the proposed model to improve the solution efficiency. Finally, IEEE14-NGS14 and IEEE118-NGS40 systems are constructed and the planning model is solved by GAMS/CPLEX. The numerical results illustrate the correctness and effectiveness of the proposed method.

A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach

This study presents a complementary model for Generation Expansion Planning (GEP). A GEP problem commonly determines optimal investment decisions in new power generation plants by minimizing total cost over a mid towards long planning horizon subjected by a set of constraints. The model aims to capture operational challenges arising when a transition towards higher shares of intermittent renewable generation is considered. It embeds a computationally expensive Operational Cost Simulation Model (OCSM), which may exhibit a high level of temporal and technical representation of the short-term operation of a power system to model the unit commitment. The emerging computationally expensive integer non-linear programming constrained optimization model is solved by a problem-customized Meta-model Assisted Evolutionary Algorithm (MAEA). The MAEA employs, off-line trained and on-line refined, approximation models to estimate the output of an OCSM to attain a near-optimal solution by utilizing a limited number of computationally expensive OCSM simulations. The approach is applied on an illustrative test case for a 15 year planning period considering the short-term operation of thermal, hydroelectric and storage units and generation from renewable energy sources. Moreover, the impact of technical resolution is examined through a simple comparative study. The results reveal the efficiency of the proposed problem-customized MAEA. Moreover, the trained approximation models exhibit a low relative error indicating that they may adequately approximate the true output of the OCSM. It is demonstrated that neglecting technical limitations of thermal units may underestimate the utilization of flexible units, i.e. thermal and non-thermal units, affecting the attained investment decisions.

Security‐constrained transmission expansion planning using linear sensitivity factors

Formulating power flow equations with linear sensitivity factors (LSFs) reduces the number of variables and constraints, and consequently, the computational burden of power systems’ optimisation problems. This study proposes a transformative, computationally efficient model for transmission expansion planning (TEP). While the existing TEP models use bus voltage angles, the proposed TEP takes advantages of LSFs to formulate an optimisation. LSFs allow to omit voltage angles from the formulation and replace all nodal power balance constraints by one equivalent constraint. Thus, the proposed model includes less number of variables and constraints compared with the classical angle-based model. These features significantly reduce computational costs of TEP and enhance its scalability, especially for large-scale systems. Load and generation uncertainties are modelled using a data-driven approach, and N − 1 security criteria are taken into account to ensure system security. All equations under normal and N − 1 conditions are considered using data of the complete network graph. Simulation results show that the proposed model provides the same results as the conventional angle-based model while being much faster (more than 58% based on the authors’ case studies) and computationally more efficient.

Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility

In the context of an increasing penetration of intermittent renewable energy sources, the impact of neglecting unit commitment constraints in generation expansion planning models has been widely assessed and demonstrated in the literature. However, the focus is often on thermal generators being the only source of flexibility, and the sensitivity to certain assumptions (e.g., system requirements and technical characteristics) has not been investigated. This paper contributes to the existing literature by revisiting the relevance of considering unit commitment constraints in generation expansion planning models for varying assumptions regarding both the available flexibility and the need for operating reserve requirements. The results indicate that if storage technologies are considered, integrating technical constraints has only a minor impact on both overall cost projections and most investments, with the exception of investments in storage technologies themselves. Furthermore, the investments in storage technologies are shown to be highly sensitive to the assumptions made regarding the assumed flexibility of thermal generators and the future need for operating reserves. These sensitivities are shown to be of an order of magnitude higher or of an equal order of magnitude compared to the impact of integrating technical constraints with continuous rather than integer commitment variables.

MILP Model of Electricity Distribution System Expansion Planning Considering Incentive Reliability Regulations

This paper aims at proposing a mixed-integer linear formulation to incorporate reliability-oriented costs into the expansion planning model of electricity distribution networks. In this respect, revenue lost associated with the undelivered energy caused by network interruptions as well as costs incurred by the widely used reward–penalty regulations is considered as the major reliability-related costs from distribution companies point of view. A set of mixed-integer linear equations is proposed to calculate the most common distribution system reliability indices, i.e., expected energy not served, system average interruption frequency index, and system average interruption duration index. It is found that these equations can also facilitate the formulation of radiality constraint in the presence of distributed generation units. Moreover, application of the proposed method is investigated through various case studies performed on two test distribution networks with 24 and 54 nodes.

A Demand-Side Management-Based Model for G&TEP Problem Considering FSC Allocation

This paper presents a model for multiperiod generation and transmission expansion planning (G&TEP) problem in the presence of uncertainties in the strategies of market participants. The effects of demand response (DR) and fixed series compensation (FSC) devices allocation are considered for peak shaving purposes and optimal utilization of transmission capacity, respectively. This may cutback the generating expansion capacity and transmission investment costs. The optimal expansion plan is achieved while the uncertainties in the generators' offers and demands' bids are considered in the market model. In this model, the DR preferences are integrated into the market clearing process of the independent system operator (ISO), which is applied to the load aggregators according to the locational marginal and market clearing prices. Shifting the demand, curtailing the peak, and onsite generation are considered as load reduction strategies in the demand response program. The ISO optimizes the decision submitted by generating companies and load aggregators in the presence of uncertainties. The proposed model is applied to the Garver, single-, two-, and four-area IEEE-RTS 24-bus systems to show the effectiveness of the multioptional DR program and the FSC devices in the dynamic G&TEP problems.

A Model for Stochastic Planning of Distribution Network and Autonomous DG Units

This article presents a mixed-integer linear stochastic model for the optimal expansion planning of electricity distribution networks and distributed generation (DG) units. In the proposed framework, autonomous DG units are aggregated and modeled using the well-known energy hub concept. In this model, the uncertainties of heat and electricity demand as well as renewable generation are represented using various scenarios. Although this is a standard technique to capture the uncertainties, it drastically increases the dimensions of this optimization problem and makes it practically intractable. In order to address this issue, a novel iterative method is developed in this article to enhance the efficiency of the optimization model. The proposed framework is further utilized to assess the benefits of the collaborative distribution network and autonomous distributed generation planning through various case studies performed on the 24-node distribution test grid. With 5.93% cost reduction, the obtained results indicate the importance of such collaborations in reaching an efficient network expansion solution. Moreover, the total planning cost for the stochastic model is 1.23% lower than the deterministic case. Various sensitivity analyses are also carried out to investigate the impacts of parameters of the proposed model on the optimal planning solution. The scalability of the model is also assessed by its implementation on the 54-node distribution test network.

Scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system

The widespread utilization of electric vehicles has inspired the emerging trend of coupled transportation and distribution system, which entails the systematic methodologies to model the new planning problems. This paper proposes a scenario-based comprehensive expansion planning model for a coupled transportation and active distribution system. With the aim of minimizing the investment and operation costs, this model determines the best alternatives, locations and sizes for candidate assets, including traffic roads, distribution lines, distribution generators, capacitor banks, static var compensators, voltage regulators, energy storage systems and charging facilities, as well as their operation strategies. First, a generated scenario method is extended to incorporate the uncertainty of the traffic flow demand. Based on multiple scenarios, the steady-state distribution of traffic flow is characterized by the Wardrop user equilibrium principle, and the corresponding equivalent constraints are derived and incorporated into the model. For active distribution system, we formulate the operation constraints for related infrastructures. Considering the interdependency between the two systems, an expansion planning model is proposed, which simultaneously optimizes the investment and operation strategies. Due to the nonlinear nature of the model, we have developed a three-dimensional piecewise linear approximation and applied second order cone relaxation to reformulate the model as a mixed-integer second order conic program; thus, the global optimal solution can be found in a reasonable time frame. Results on a test system reveal that the variations of both systems have some influences on each other, indicating the significance of considering the interdependencies between transportation and active distribution system.

Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

Decision makers rely on models to make important regulatory, policy, and investment decisions. For power systems, these models must capture (i) the future challenges introduced by the integration of large quantities of variable renewable energy sources and (ii) the role that energy storage technologies should play. In this paper, we explore several different approaches to selecting representative days for generation expansion planning models, focusing on capturing these dynamics. Further, we propose a new methodology for adjusting the outputs of clustering algorithms that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peak demand is captured. This weighting methodology reduces the magnitude of the error in the representative day based generation expansion planning models estimation of costs by 61% on average. The results also demonstrate the importance of carefully performing the clustering of representative days for both system costs and technology mix. In most cases improvements to the total cost of different representative day based expansion plans are realised where conventional generation capacity is substituted for energy storage. Based on the energy storage technology selected we conclude this capacity is being used to address ramping challenges as opposed to shifting renewable generation from off to on peak periods, reinforcing the importance of capturing detailed intraday dynamics in the representative day selection process.

Expansion planning model of multi-energy system with the integration of active distribution network

As an effective pattern to promote efficient use of energy, multi-energy system has become the main focus of development. On the other hand, active distribution network is now also the promoting concept for developing power distribution system, since the active network management technologies can make the network more flexible and controllable. To facilitate and utilize the advantages of both systems, this paper proposes an expansion planning model for multi-energy system integrating active distribution network, natural gas network and energy hub; and the positive impact of active network managements on expanding multi-energy system is originally investigated. With the aim of minimizing the total cost over the planning horizon, this model centers on the optimal determination of the type, location and size of all the infrastructures, where the active network managements are modeled and incorporated. In addition, in order to improve the robustness of the planning results, a probabilistic scenario generation approach is proposed to test the model. In order to solve the proposed mixed-integer nonlinear model, the second order cone programming as well as a modified piecewise linearization approach is applied to convert the original model to a mixed integer second order cone programming model. A multi-energy system (including a modified IEEE 33-node distribution system, a new 23-node gas system and 8 energy hubs) are employed to verify the effectiveness of the prosed model and methods. The simulation results exhibit the superiority of the joint expansion planning model for multi-energy system and the beneficial impact of considering active network management.

Short-Circuit Constrained Power System Expansion Planning Considering Bundling and Voltage Levels of Lines

System expansion planning (SEP) models do not generally represent voltage levels, bundled conductors in transmission lines, and short-circuit limits. These modeling assumptions may result in suboptimal planning outcomes. To overcome this potential flaw, we propose a short-circuit constrained dynamic SEP (SC-SEP) that allows for investment decisions at different stages of the planning horizon and includes a detailed representation of voltage levels, alternative bundled conductor options per line and short-circuit limits. An effective linearization technique is used to transform the resulting mixed-integer nonlinear model into a mixed-integer linear one. The accuracy of the proposed model and the effectiveness of the proposed linearization technique are illustrated using the IEEE 39-bus system.

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.

Two‐stage nested bilevel model for generation expansion planning in combined electricity and gas markets

The growing utilisation of natural gas and renewable energy resources brings more challenges to generation expansion planning problems. Short-term operational constraints are equally important for long-term capacity planning. This work studies the interdependence between electricity and natural gas systems and presents a combined market mechanism that allows two-stage energy trading and planning based on asynchronous electricity and gas markets. In the first stage, the gas market is cleared with the objective of maximum social welfare (lower level), at the same time obtaining the optimal strategies offered by gas producers and gas-fired units (upper level). In the second stage, generation companies and consumer companies aim to maximise their corresponding profits in the planning horizon (upper level), and electricity market is cleared on principle of maximum social welfare (lower level). Then, the authors develop a modified alternative direction method of multiplier algorithm to solve the two-stage nested bilevel model. To improve operational flexibility of expansion plans, uncertainties of renewable energy generation and integrated demand response are also included and analysed in different risk scenarios. Case studies validate the effectiveness of the proposed methodology.

Multi-objective generation expansion and retirement planning using chaotic grasshopper optimisation algorithm

ABSTRACTAn efficient, cost-effective multi-objective generation expansion planning (GEP) with Load Forecast (LF) uncertainty is described and evaluated in this article. The aspects that are considered in this multi-objective GEP model are cost, gas emission, energy price risk, fuel consumption and reliability of the generating units. The retirement of existing generating units is included in the projected model as these units reach their maximumLlifetime. To solve the projected generation expansion and retirement planning model, a Chaotic Grasshopper Optimisation Algorithm (CGOA) is proposed and it is applied into 6-, 14- and 24-year planning periods, and numerical result shows that GEP provides a cost-effective solution with less emission.

A MILP Model for Incorporating Reliability Indices in Distribution System Expansion Planning

Reliability cost is considered as an inevitable criterion in expansion planning studies of distribution systems. However, nonlinear expressions of reliability indices aggravate complexity of planning studies. To address this issue, this letter proposes a novel method to linearize mathematical model of the reliability-based distribution expansion planning problem. Using this variant of reliability indices, reliability costs can easily be involved in mixed-integer linear programming model of distribution expansion planning. Validity of the derived expressions is tested by simulation results.

A Metamodeling Framework for Quantifying Health Damages of Power Grid Expansion Plans.

In this paper, we present an analytical framework to establish a closed-form relationship between electricity generation expansion planning decisions and the resulting negative health externalities. Typical electricity generation expansion planning models determine the optimal technology–capacity–investment strategy that minimizes total investment costs as well as fixed and variable operation and maintenance costs. However, the relationship between these long-term planning decisions and the associated health externalities is highly stochastic and nonlinear, and it is computationally expensive to evaluate. Thus, we developed a closed-form metamodel by executing computer-based experiments of a generation expansion planning model, and we analyzed the resulting model outputs in a United States Environmental Protection Agency (EPA) screening tool that approximates the associated human health externalities. Procedural guidance to verify the accuracy and to select key metamodel parameters to enhance its prediction capability is presented. Specifically, the metamodel presented in this paper can predict the resulting health damages of long-term power grid expansion decisions, thus, enabling researchers and policy makers to quickly assess the health implications of power grid expansion decisions with a high degree of certainty.