Expansion Planning Model Research Articles

An Enhanced MILP Model for Multistage Reliability-Constrained Distribution Network Expansion Planning

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability.

An Efficient Data Driven Model for Generation Expansion Planning with Short Term Operational Constraints

Generation expansion planning (GEP) models have been useful aids for long-term planning. Recent growth in intermittent renewable generation has increased the need to represent the capability for non-renewables to respond to rapid changes in daily loads, leading research to bring unit commitment (UC) features into GEPs. Such GEP+UC models usually contain discrete variables which, along with many details, make computation times impractically long for analysts who need to develop, debug, modify and use the GEP for many alternative runs. We propose a GEP with generation aggregated by technology type, and with the minimal UC content necessary to represent the limitations on generation to respond to rapid changes in demand, i.e., ramp-up and ramp-down constraints, with ramp limits estimated from historical data on maximum rates of change of each generation type. We illustrate with data for the province of Ontario in Canada; the GEP is a large scale linear program that solves in less than one hour on modest computing equipment, with credible solutions.

Robust optimization for energy transition planning in manufacturing firms: An integrated model addressing economic and environmental issues

Moving away from fossil fuels to cleaner energies is a big challenge in different industries due to its complexities and the huge capital investment needed. Manufacturers therefore need efficient energy transition planning to enhance their energy basket. In this regard, capacity expansion planning models can provide an appropriate context in which operations decisions can be integrated into energy transition planning. To pursue this, we develop an optimization model to integrate capacity planning, energy transition planning, while focusing on economic and environmental issues. Due to the effect of operations and financial decisions on both strategic and tactical planning, we integrate strategic and tactical planning horizons in this paper. Furthermore, to deal with the inherent uncertainty in the proposed model, a robust optimization approach will be developed. This approach is used to handle demand uncertainty while overcoming the over-conservativeness issue. The practicality and the effectiveness of the proposed approach are illustrated by implementing it on a real case study. Additionally, Monte Carlo simulations are utilized to assess the performance of the robust solutions. The results show that robust solutions may lead to a drop in the simulated objective function by 14% in comparison with deterministic solutions when uncertainty level is high.

Accessible Modeling of the German Energy Transition: An Open, Compact, and Validated Model

We present an easily accessible model for dispatch and expansion planning of the German multi-modal energy system from today until 2050. The model can be used with low efforts while comparing favorably with historic data and other studies of future developments. More specifically, the model is based on a linear programming partial equilibrium framework and uses a compact set of technologies to ease the comprehension for new modelers. It contains all equations and parameters needed, with the data sources and model assumptions documented in detail. All code and data are openly accessible and usable. The model can reproduce today’s energy mix and its CO2 emissions with deviations below 10%. The generated energy transition path, for an 80% CO2 reduction scenario until 2050, is consistent with leading studies on this topic. Our work thus summarizes the key insights of previous works and can serve as a validated and ready-to-use platform for other modelers to examine additional hypotheses.

A new approach for coupling the short- and long-term planning models to design a pathway to carbon neutrality in a coal-based power system

Transformation towards climate-neutral energy systems is becoming a challenge worldwide. Most often, strategic planning for the mid- and long-term is tackled with the use of generation expansion optimization models, which have limited capabilities to track in detail power system operation. On the other hand, the unit commitment and economic dispatch models consider many technical constraints but are usually used for short-term planning. The methodological approach presented in this paper aims to improve the planning capabilities by the combination of generation expansion and short-term planning models into one modelling system. Two models were coupled, namely TIMES-PL and MEDUSA, to design a pathway to carbon neutrality in the Polish power system. Three energy scenarios elaborated in this work show that a 95 % reduction in carbon dioxide emissions from the public electricity and district heat production sectors is possible by 2050. The proposed approach improved the robustness of elaborated energy scenarios as in their first versions two of them turned out to be technically infeasible. Detail simulations of the system operation showed that load balancing is possible once 15–20 GW of electrical capacity is installed in dispatchable power generation technologies by 2050 in addition to energy storage and combined heat and power plants.

Hybrid robust/stochastic transmission expansion planning considering uncertainties in generators’ offer prices: A second-order cone program approach

The aim of this paper is to propose a hybrid robust/stochastic model for transmission expansion planning (TEP) problem. The uncertainties related to demand and wind are modelled via stochastic programming (SP), while the uncertainties associated with the generators’ offer prices are modelled via robust optimization (RO). In this sense, an ellipsoidal uncertainty set is considered which the offer price values are known to lie in these given ellipsoids. The model presented is then divided into a master problem and several independent subproblems using a tailored implementation of Bender's decomposition (BD) method. Robust linear constraints of the lower level problems are then transformed into a convex second-order cone programming (SOCP) problem. We will apply the proposed model to IEEE-118 bus power system showing that generators’ offer price and their correlation can considerably affect the optimal plan of the transmission planning. It is observed that considering correlation among generators’ offer prices would not necessarily yield more expensive expansion plan in comparison with the case where correlation is ignored. Indeed, increase or decrease in investment cost depends on the entries of the variance-covariance matrix, network topology and the location of generators. Moreover, the results show the computational efficiency of the robust convex model.

Hybrid‐adaptive differential evolution with decay function applied to transmission network expansion planning with renewable energy resources generation

As the country implements the reform of the electricity market and a large number of renewable energy resources (RES) are connected to the grid, the model and algorithm of traditional transmission network expansion planning (TNEP) are not suitable. A model of TNEP is built, in which the investment cost of new lines and the market-based annual congestion surplus are selected as objective functions. A probabilistic DC power flow method is used to describe uncertainties of RES generation. A new algorithm, Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF), is applied to solve the model of TNEP for the first time. Analysis and comparison in the IEEE 18-bus system and the 52-bus system in an area of Sichuan Province prove the feasibility, effectivity, and practicability of the model and solving algorithm. Some comparisons are performed using other variants of Differential Evolution (DE) and a swarm intelligence optimization algorithm to demonstrate the performance superiority of the new algorithm and new application. It is worth mentioning that in the 52-bus system of an area of Sichuan Province, the investment cost obtained by the new algorithm is at least one or two orders of magnitude lower than other algorithms.

Modeling investment decisions from heterogeneous firms under imperfect information and risk in wholesale electricity markets

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors’ forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms’ heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm’s own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent.

Research on low-carbon economic expansion planning of electric-gas interconnected integrated energy system containing power to gas

ABSTRACT Under the goal of carbon peaking and carbon neutralization, the integrated energy system will accelerate the cleaner and low-carbon structure transition. In addition, high carbon dioxide emissions in some industrial parks give rise to a series of environmental problems. The emergence of power-to-gas technology provides new insight into solving the problem. This empirical research is an attempt for lower carbon emissions and greater application of renewable energy, and a proposal for an expansion planning model for integrated energy systems considering different sources of CO2. With the introduction of economic conversion coefficients, carbon dioxide emission is converted into economic dimensions and the other cost together to form the objective function with the minimum cost. The constraints of energy balance, energy network, and equipment output are considered. Finally, through the simulation of the calculation example, the total cost of the carbon capture scenario is reduced by 9.9%, and the carbon emission 24.6% compared with the existing park, which verifies that the proposed model considering capturing carbon dioxide can effectively take into account the low carbon and economic efficiency, and meanwhile enhance the renewable energy accommodation.

An integrated transmission expansion planning and battery storage systems placement - A security and reliability perspective

This paper presents a new mathematical approach for co-planning of transmission expansion planning and battery storage systems placement. An optimal placement of grid-scaled battery storage systems can help transmission systems in congestion managements and improve power system security. In addition, battery storage systems can increase power system reliability in contingency conditions. In this paper a security and reliability viewpoint is implemented for the simultaneous transmission expansion planning and the optimal placement of battery storage systems. The N-1 security constraints are applied to the expansion planning model. The reliability indices evaluated from second order outages are also involved in the decision making process. The Benders’ decomposition approach is used to reduce the computational burden of the proposed model and make it applicable for large scaled power systems. The IEEE 24-bus test system is used to evaluate the applicability of the proposed method.

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

CVaR‐based generation expansion planning of cascaded hydro‐photovoltaic‐pumped storage system with uncertain solar power considering flexibility constraints

The development of a high solar energy penetrated power system requires considerable flexibility to hedge the risk of solar power curtailment and power shortage. This paper explores how the generation portfolio of cascaded hydro-photovoltaic-pumped storage (CH-PV-PS) generation system will be appropriately designed to balance the overall planning costs and operational flexibility constraints. The proposed study relies on the generation expansion planning (GEP) model of the CH-PV-PS system, considering a full set of flexibility constraints. An index designated ramp-capability reserve shortage (RCRS) based on the conditional value at risk (CVaR) method is introduced to quantify the risk of solar power and load uncertainty. The piecewise linearization method and triangle method are developed to accommodate the non-linear terms in the proposed model. Finally, the case studies are conducted to demonstrate the applicability and effectiveness of the proposed model.

Long term storage in generation expansion planning models with a reduced temporal scope

To reduce the computation time of Energy System Optimization Models and Generation Expansion Planning Models operational detail is typically limited to several hours, days, or weeks in a year selected using Time Series Aggregation methods. We compare time series aggregation methods and generation expansion planning models which aim to capture the value of long-term storage for the first time in the literature. Time Series Aggregations methods were compared by varying the number of representative periods and then running a full year generation expansion planning model on novel synthetic time series. Generation Expansion Planning Models were run on selections and ordering of representative periods in order to compare them. Our results suggest that approximating the full-year time series does not necessarily translate to approximating the full-year generation expansion planning solution and that selecting hours or days is a greater determinant of performance than the time series aggregation method itself. Two of the generation expansion planning models considered, Enhanced Representative Days and Chronological Time Period Clustering, could capture the value of long-term storage, though over or underinvestment in long-term storage by more than a factor of 2 was also possible and the latter formulation exhibited a clear bias towards long-term storage. Based on these results we formulate recommendations for modelers seeking to include long-term storage in generation expansion planning models.

A linearized transmission expansion planning model under N − 1 criterion for enhancing grid‐scale system flexibility via compressed air energy storage integration

The concept of flexibility is defined as the power systems’ ability to effectively respond to changes in power generation and demand profiles to maintain the supply–demand balance. However, the inherent flexibility margins required for successful operation have been recently challenged by the unprecedented arrival of uncertainties, driven by constantly changing demand, failure of conventional units, and the intermittent outputs of renewable energy sources (RES). Tackling these challenges, energy storage systems (ESS) as one important player of the new power grids can enhance the system flexibility. It, therefore, calls for an efficient planning procedure to ensure flexibility margins by considering ESS's role in modern power systems. This paper proposes a novel mixed integer linear programming (MILP) model for transmission expansion planning (TEP) framework taking into account the role of compressed air energy storage (CAES) integration on improvements in system flexibility. The proposed framework is housed with a quantitative metric of grid-scale system flexibility, while a new offline repetitive mechanism is suggested to account for the N − 1 reliability criterion. The model is applied to different test systems, where the numerical results demonstrate the impacts of CAES units on system flexibility, investment plans, and the total costs.

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

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

Generation expansion planning considering efficient linear EENS formulation

Power system equipment outages are one of the most important factors affecting the reliability and economy of power systems. It is crucial to consider the reliability of the planning problems. In this paper, a generation expansion planning (GEP) model is proposed, in which the candidate generating units and energy storage systems (ESSs) are simultaneously planned by minimizing the cost incurred on investment, operation, reserve, and reliability. The reliability cost is computed by multiplying the value of lost load (VOLL) with the expected energy not supplied (EENS), and this model makes a compromise between economy and reliability. Because the computation of EENS makes the major computation impediment of the entire model, a new efficient linear EENS formulation is proposed and applied in a multi-step GEP model. By doing so, the computation efficiency is significantly improved, and the solution accuracy is still desirable. The proposed GEP model is illustrated using the IEEE-RTS system to validate the effectiveness and superiority of the new model.

Power system planning based on CSP-CHP system to integrate variable renewable energy

The renewable energy penetration level of power grid is forecasted by National Renewable Energy Laboratory (NREL) to reach 60% in 2050, which will bring a great challenge to accommodate the variability and uncertainty of variable renewable energy (VRE). In this paper, a hybrid CSP-CHP system is proposed to provide additional operational flexibility for power system to integrate renewable energy. First, the energy flow framework and the operational flexibility of the hybrid CSP-CHP system are analyzed. Second, a stochastic two-stage unit expansion and generation planning model based on proposed hybrid system is adopted to optimize the power system planning by considering generation technologies and policy orientation. Finally, a case study on a modified 40-bus power system is performed to research the value of hybrid CSP-CHP in 2020–2050 power system planning. Results show that the hybrid CSP-CHP plant will make up an important portion of renewable generation, and the high value of the proposed system is reflected in operational flexibility and application prospect.

Closing the void in energy planning modeling: Integrating local realities

Closing the void in energy planning modeling: Integrating local realities

Baseline Model for Electricity Generation Expansion Planning for The Philippines: A Leap Model Application

Philippines is one of the fastest-growing economies and most populated countries globally, greatly affecting its energy demand. The increase in demand leads to an increase in the electricity generation sector's capacity to maintain the supply-demand balance. Thus, this may lead to power shortage, high generation costs, and increased greenhouse gas emissions without proper energy planning. This study aims to create a Long-Range Energy Alternatives Planning System (LEAP)-based Baseline Model for the Philippines' generation expansion planning. The model will serve as a tool for the government and energy planners to develop a long-term generation expansion planning model. The model simulates the technological, economic and environmental aspect of the existing Philippine power generation system that can be used as a baseline for developing a Business-As-Usual Scenario and Alternative Scenario models to assess different energy policy pathways. The developed LEAP model following an independent multi-regional and national scale structure that is based on the Philippine grid and off-grid based regions showed an accurate result with the actual data. The model was able to replicate the actual power generation through the set-up of basic parameters. Development of Philippine LEAP structure; and the establishment of the demand, transformation, and resources module’s structure are based on the set of data input parameters. The baseline model developed can be used for continuing studies such as impact evaluation of energy policy for long term power generation expansion planning.

A coordinated scheme for transmission and distribution expansion planning: A Tri-level approach

In traditional power system planning problems, transmission and distribution networks are separated, which is not suitable for future power systems due to the rapid development of active distribution networks. This paper presents a hierarchy framework to optimize the transmission and distribution network expansion in a simultaneous manner. The proposed decentralized decision-making architecture forms a Tri-level structure where the first level is the transmission expansion planning (TEP) problem solved by the transmission system operator (TSO) as the leader, while the second level is the distribution expansion planning (DEP) problem solved by the distribution system operators (DSOs) as the second level followers. The third level problem is the optimal economic dispatch problem solved by the independent system operator (ISO) as the third level follower. The economic dispatch problem is replaced by its primal-dual (PD) formulation leading to a bilevel multi-follower mixed-integer linear programming (BMF-MILP) problem. Then, by using multi-parametric programming (MPP) method, the BMF-MILP problem is recast as several single level independent optimization problems. Numerical experiments are shown to corroborate the efficiency and tractability of the presented model. The results show the new coordinated transmission and distribution expansion planning (CTDEP) model provides additional flexibility and reduces the total cost.