Short-term Operational Constraints Research Articles

Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems

This paper presents a flexibility-oriented generation expansion planning (GEP) model with the goal of increasing renewable energy penetration. The model incorporates GEP and unit commitment while considering improved reserve requirements. Utilizing GEP and unit commitment results in considering short-term operational constraints. Furthermore, given the increase in renewable portfolio standards (RPS) and the need for accurate reserve requirements determination, it is essential to consider improved reserve requirements in the proposed model. Moreover, this model utilizes a metric that measures power system flexibility deficiency and leads to flexible power system expansion by integrating it into the objective function. The flexibility-oriented GEP model is developed using mixed-integer linear programming and applied to a scaled-sized case study representing the national power system of Iran with a twenty-year planning horizon. The results show that neglecting improved reserve requirements leads to plans short on flexibility, and considering these requirements substantially declines the upward flexibility violation costs by 68% under the base scenario and 87% under increasing non-hydro RPS scenario. Furthermore, using the flexibility metric coupled with improved reserve requirements changes the optimal path for power system expansion and reduces the risk of flexibility deficiency. Also, increasing RPS by 35% would strongly depend on developing fast-response dispatchable generation units in the years before renewable energy resources expansion; however it doesn’t impose a significant extra cost of planning and the total cost will increase just by 9.43%.

Two-Stage Stochastic Optimization Model for Multi-Microgrid Planning

This paper presents a Two Stage stochastic Programming (TSSP) model for the planning of Multi-Microgrids (MMGs) in Active Distribution Networks (ADNs). The model aims to minimize the total costs while benefiting from interconnections of Microgrids (MGs), considering uncertainties associated with electricity demand and Renewable Energy Sources (RESs). The associated uncertainties are analyzed using Geometric Brownian Motion (GBM) and probability distribution functions (pdfs). The model includes long-term purchase decisions and short-term operational constraints, using Geographical information Systems (GIS) to realistically estimate rooftop solar limits. The planning model is used to study the feasibility of implementing an MMG system consisting of 4 individual Microgrids (MGs) at an ADN in a municipality in the state of São Paulo, Brazil. The results show that the TSSP model tends to be less conservative than the deterministic planning model, which is based on simple and pessimistic reserve constraints, while performing faster than a simple Stochastic Linear Programming (SLP) algorithm, with higher accuracy.

Geographic-information-based stochastic optimization model for multi-microgrid planning

This paper presents a model for the realistic planning of multi-microgrids in the context of Active Distribution Networks with the assistance of Geographic Information Systems. The model considers the distribution system grid as well as the geographic features of the Region of Interest. It also includes long-term purchase decisions and short-term operational constraints, and considers uncertainties associated with electricity demand and Renewable Energy Resources using an existing Two-Stage Stochastic Programming approach. Geographic Information Systems along with Deep Learning are used to estimate the areas of rooftops within the Region of Interest and model the Low Voltage grid. The planning model is used to study the feasibility of implementing a multi-microgrid system consisting of 4 individual microgrids at an Active Distribution Network in a municipality in the state of São Paulo, Brazil. The results of the model presented in this paper are compared with the results obtained using Monte Carlo Simulations and an existing, less detailed, Two Stage Stochastic model. It is demonstrated that the stochastic solutions are close to those obtained with Monte Carlo at a lower computational cost, and that the use of Geographic Information allows to determine both the capacity and location of the PV panels, batteries, and distribution transformers on the microgrids grid, thus providing more precise and useful planning results.

Risk-Averse Stochastic Midterm Scheduling of Thermal-Hydro-Wind System: A Network-Constrained Clustered Unit Commitment Approach

With the increasing penetration of renewable energy, designing the efficient midterm (i.e., annual) schedule for multi-source power systems is valuable in providing substantial cost reductions and flexibilities while ensuring system security against uncertainties of renewables. This paper presents a risk-averse two-stage stochastic midterm scheduling model for thermal-hydro-wind systems against short-term uncertainties of renewable generation. A network-constrained clustered unit commitment (NC-CUC) model is proposed to effectively incorporate short-term operation constraints into the midterm scheduling model, for improving the solution accuracy while reducing computational burden. Specifically, as the traditional CUC model cannot ensure consistent unit commitment status of generators across all scenarios, a power flow-based unit clustering method is proposed to improve the accuracy of short-term operation results. In addition, the conditional value-at-risk (CVaR) is used to quantify the system operational risk caused by uncertainties of wind generation and natural water inflows. Case studies show that the risk-averse two-stage stochastic midterm scheduling model can ensure operation dynamics and solution efficiency, and the proposed clustering method can achieve a tradeoff between solution accuracy and computational burden for the stochastic network-constrained CUC model.

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.

A Multi-objective Optimization Framework for Dynamic Planning of Energy Hub Considering Integrated Demand Response Program

The integrated operation of multi-carrier energy systems has attracted special attention due to its increased efficiency, improved flexibility and reduced pollution. It should be noted that accurate and optimal design of these systems will have a high impact on their optimal operation. Hence, this paper presents a multi-objective optimization framework for long-term planning of energy hub, in which equipment degradation and integrated demand response (IDR) programs are considered. The planning problem has been solved for 20 years and all short-term operation constraints have been taken into account. The problem is modeled using the fuzzy max-min method as a three-objective optimization problem, in which the objective functions include total cost, emission and hub losses. The simulation results demonstrate that the IDR program has reduced the total cost by about 9% by reducing equipment capacity and operating costs. In addition, the results illustrate that the reduction of 29% and 31% of losses and emission, led to a 49% increase in total costs. Overall, the results demonstrate that the proposed model, considering the equipment degradation and the annual load growth, leads to the optimal design of the hub structure and guarantees the supply of load until the end of the planning period.

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.

A Time-Sequence Simulation Method for Power Unit’s Monthly Energy-Trade Scheduling with Multiple Energy Sources

The uncertainty of new energy output from wind power is rarely considered in the monthly energy-trade scheduling. This causes many problems since the new energy penetration level increases. The fairness of the scheduled energy for the power suppliers is difficult to guarantee. Because the actual power system operation is far away from scheduling when the monthly energy-trade schedule is carried out, unnecessary wind curtailment might occur, and even the feasibility of monthly energy-trade schedule might not be guaranteed. This affects the security and reliability of the power system operation. In this paper, a new time-sequence simulation method for the monthly energy-trade scheduling is proposed, which considers the new energy power forecasting characteristic and the computational load problem of hourly energy-trade simulation in the remaining months. The proposed method is based on a segment modelling strategy. The power generation in the scheduling month is optimized hourly, and the energy generation is optimized in the subsequent months on a monthly basis. For the scheduling month, accurate cost function is applied in the objective function, and detailed short-term operation constraints and the new energy forecasting results are considered, which can guarantee the feasibility of the new monthly energy-trade scheduling and lay a solid foundation for daily dispatching. For the subsequent months, since the load forecast accuracy is lower and no wind power forecasting results could be used, the rough cost function is applied, and only monthly constraints are considered. To ensure a balance in the execution progress of each power generating entity, the simulation time-scale is set as the remainder of the months in the study year. The new approach ensures the fairness of power execution progress and improves the new energy consumption level. A case study was used to verify the feasibility and effectiveness of the proposed method, which provides a theoretical reference for the monthly electrical energy-trade scheduling.

Long-term energy system planning considering short-term operational constraints

The intermittent nature of renewable energy sources (RESs) brings formidable challenges in the operation of power system. Long-term energy system planning models overlook the impact of renewable intermittency on system operations due to the computational burden associated with large model size and long planning horizon. Hence, strategies such as soft-linking multiple models are developed, but they do not assure the convergence and optimality of such incoherent modeling framework. In this context, this paper utilizes unit commitment (UC) extension of TIMES modeling framework to integrate operational constraints directly in a long-term power system planning model. This strategy eliminates the complexity of handling multiple models. Results indicate that incorporation of UC constraints improve the performance of conventional generators in terms of increased capacity utilization, and help to assess flexibility requirements with high RESs. Energy storage provides the balancing and flexibility needs with stringent generator constraints. Sensitivity analysis shows that improved flexibility of thermal generators enables increased renewable penetrations.

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-stage contingency-constrained co-planning for electricity-gas systems interconnected with gas-fired units and power-to-gas plants using iterative Benders decomposition

The increasing share of variable renewable energy sources and the improving requirements on system security and reliability are calling for important changes in the integrated energy systems. Hence this paper proposes a multi-stage contingency-constrained co-planning model for electricity-gas systems interconnected with gas-fired units and power-to-gas plants considering the uncertainties of load demand and wind power. The proposed model considers the long-term co-planning for electricity-gas systems with the short-term operation constraints and N-1 reliability criterion, which is formulated as a mixed-integer linear programming problem. First, a scenario reduction method based on affinity propagation clustering is utilized to handle the uncertainties. Afterwards, the N-1 criterion is tackled by a contingency screening method based on line outage distribution factors. Finally, an iterative Benders decomposition method is developed to divide the programming problem into one master investment problem and three subproblems. The iterative process will continue between master problem and each subproblem until an economic, reliable and fuel-supply feasible solution is obtained. In order to verify the feasibility of the proposed model and the effectiveness of the proposed method, case studies are conducted on two different scales of electricity-gas systems.

Coordination of Short-Term Operation Constraints in Multi-Area Expansion Planning

This paper presents a comprehensive expansion planning algorithm of generation and transmission components in multi-area power systems. The objective is to minimize the total system cost in the planning horizon, comprising investment and operation costs and salvage values subject to long-term system reliability and short-term operation constraints. The multi-area expansion planning problem is decomposed into a planning problem and annual reliability subproblems. The planning decisions calculated in the planning problem would also satisfy the short-term operation constraints. A detailed model of thermal and hydro units is considered using the mixed-integer programming (MIP) formulation. In addition, a multi-state representation for the expansion planning of renewable energy units is explored. The proposed approach considers customers' demand response as an option for reducing the short-term operation costs. The planning problem solution is applied to the annual reliability subproblems which examine system reliability indices as a post-processor. If the reliability limit is not satisfied, additional reliability constraints will be introduced which are based on the sensitivity of system reliability index to investment decisions. The new reliability constraints are added to the next iterations of the planning problem to govern the revised plan for the optimal expansion. Numerical simulations indicate the effectiveness of the proposed approach for solving the operation-constrained multi-area expansion planning problem of practical power systems.