Information Gap Decision Theory Research Articles

A risk-averse approach for distribution locational marginal price calculation in electrical distribution networks

As distributed generation (DG) systems continue reshaping power networks, optimizing pricing strategies at DG buses using the distribution locational marginal price (DLMP) concept becomes critical for economic efficiency and the optimal operation of the network. This paper proposes a novel approach that combines Information Gap Decision Theory (IGDT) and Particle Swarm Optimization (PSO) to address these challenges. IGDT offers a robust framework for optimizing pricing in the presence of uncertainties and incomplete information in the distribution network. It quantifies and models these uncertainties, enabling the creation of adaptable pricing strategies that respond effectively to changes in DG output and market conditions. To further refine pricing strategies, PSO is integrated into the IGDT framework. PSO's iterative approach allows for the discovery of optimal pricing parameters at DG buses, striking a balance between maximizing DG owner profits and ensuring system optimality. Extensive simulations on two realistic distribution network models validate the effectiveness of the proposed IGDT-PSO approach. It demonstrates that DG owners can reliably achieve their predefined profit targets while also adhering to the objectives of the distribution network.

Optimum exploitation of multiple energy system using IGDT approach and risk aversion strategy and considering compressed air storage with solar energy

The concept of an energy hub within a microgrid has garnered significant attention from investors due to its potential for efficiently managing diverse energy sources and accurately forecasting energy prices. This study presents a model for an energy hub that facilitates the optimal operation of a microgrid with multiple energy carrier infrastructures under future-day scenarios. The primary objective of the optimization process is to minimize operating costs, environmental impacts, and technical limitations. The proposed energy hub model integrates both distributable generation, such as a Combined Cooling, Heating, and Power (CCHP) unit, and non-distributable generation sources, including wind turbines and photovoltaic systems. Additionally, it incorporates various energy storage components, namely Ice Storage Conditioner (ISC), Thermal Energy Storage (TES) systems, and Electric Vehicles (EVs). Furthermore, the study investigates the impact of a novel energy storage system called Solar Powered Compressed Air Energy Storage (SPCAES) on the operational and environmental performance of the energy hub. To address uncertainties stemming from predicted and actual variables, a two-level stochastic optimization problem is formulated based on the Information Gap Decision Theory (IGDT) with a Risk Aversion (RA) strategy. The overarching aim is to mitigate the risks associated with the information gap confronted by microgrid operators. Subsequently, the two-level stochastic optimization problem is transformed into a single-level problem using the Karush-Kuhn-Tucker method. The proposed framework employs the RA-IGDT method and utilizes the Elephant Herd Optimization (EHO) algorithm to effectively solve the optimization problem. By applying this proposed framework to a typical energy hub, this research demonstrates the effectiveness of energy storage systems in reducing operating costs and greenhouse gas emissions during daily energy management. The study highlights the potential of energy hubs and storage systems for cost-effective and eco-friendly microgrid energy management. Scenario 4, featuring the proposed energy hub with SPCAES and ISC storage, demonstrates a 15 % reduction in operating costs and a notable 20 % increase in peak-hour electrical power for sale, underscoring its economic and operational benefits.

Integration of mobile power-hydrogen storage systems in distribution-level networks: A fuzzy information gap optimization framework

In recent years, renewable energy sources (RESs) have had unquestionably significant benefits. However, maximum penetration of RESs has always been challenging due to their intermittent nature. To address this challenge, this study advances a framework for the flexibility of the distribution network (DN) by employing transportable electrical storages (TESs) and transportable hydrogen storages (THSs). To do so, the TESs are considered to transfer electrical energy among high-penetrated and high-demand points. In addition, curtailed renewable power can be converted to green hydrogen and transferred to hydrogen refueling stations (HRSs) using the THSs. To further flexibility, this work considers an incentive-based demand response (DR) program and vehicle-to-parking lot (V2PL) capable plug-in electric vehicles (PEVs). Moreover, a binary-based method is developed to carry out the trajectory planning of the TESs and THSs and the Floyd algorithm is exploited to minimize traveling and simulation time. Furthermore, the fuzzy information gap decision theory (IGDT) is proposed to enhance the scheduling robustness against the uncertainty of RESs. The obtained results corroborated the positive impact of the TESs and THSs by declining the operation cost by about 4.8 %. Moreover, the proposed uncertainty management model is quite robust against RESs’ uncertainty by achieving about 37.5 % less operation cost.

Enhancing resilience by outage management strategy based on semidefinite programming relaxation method in unbalanced microgrids

During electrical power outages caused by extreme events, microgrid operators (MGOs) attempt to restore as much electrical load as possible. This work suggests a strategy for outage management (OM) to improve microgrid resilience by using two optimal actions: distribution feeder reconfiguration (DFR) and scheduling of distributed energy resources (DERs). After a line fault, the radial network topology is determined by the proposed model using the rank of the incidence matrix. The proposed model is formulated by an analytical optimization method created by semidefinite programming (SDP) relaxation. The SDP changes the non-convex and nonlinear model suggested for OM into an approximated convex model, which commercial software applies. The proposed model considers the uncertain behavior of non-dispatchable DERs and the electrical demands that deal with an information gap decision theory (IGDT) based on a risk-averse strategy. To improve the proposed model's flexibility, the shortened electrical demand as a demand response program (DRP) is considered. The aim of optimization is the minimization of the accumulative cost for dispatchable DER operation and load reduction. The suggested SDP model is figured out using the MOSEK solver in GAMS software. Using the offered model in the 69-bus unbalanced test system displays that the SDP model averagely decreases total operation cost and execution time by 1.04% and 61.29% on all scenarios in comparison with the conventional GAMS model.

Credibility Theory-Based Information Gap Decision Theory to Improve Robustness of Electricity Trading under Uncertainties

In the backdrop of the ongoing reforms within the electricity market and the escalating integration of renewable energy sources, power service providers encounter substantial trading risks stemming from the inherent uncertainties surrounding market prices and load demands. This paper endeavors to address these challenges by proposing a credibility theory-based information gap decision theory (CTbIGDT) to improve robustness of electricity trading under uncertainties. To begin, we establish credibility theory as a foundational risk assessment methodology for uncertain price and load, incorporating both necessity and randomness measures. Subsequently, we advance the concept by developing the CTbIGDT optimization model, grounded in the consideration of expected costs, with the primary aim of fortifying the robustness of electricity trading practices. The ensuing model is then transformed into an equivalent form and solved using established standard optimization techniques. To validate the efficacy and robustness of our proposed methodology, a case study is conducted utilizing a modified IEEE 33-node distribution network system. The results of this study serve to underscore the viability and potency of the CTbIGDT model in enhancing the effectiveness of electricity trading strategies in an uncertain environment.

Risk-aware day-ahead planning of a zero energy hub integrated with green power-to-hydrogen technology using information gap decision theory and stochastic approach and considering demand side elasticity

Environmental issues and rapid load growth have led to increasing renewable resources infiltration toward achieving zero energy infrastructures; however, the uncertainty of such components increases the interactions between the local grid and energy markets for sustainable operation. In this study, day-ahead planning of a zero energy hub composed of wind turbine, photovoltaic, electric heat pump, boiler, chiller and storage units is optimized under the uncertainty of wind and solar units and energy market trading. In the designed structure, the required fuel for the local network is supplied through a power-to-hydrogen system to manage carbon emissions. In order to model the uncertainties and analyze the risk of decisions, a hybrid method consisting of the stochastic approach and information gap decision theory is applied. Furthermore, the impact of demand side elasticity for electrical, heating and cooling loads is evaluated. The results show that 10% of load participation significantly improves energy management actions and decreases operation costs by about 15.6%. The simulations also approve that the hybrid method handles the uncertainties under different conditions, where the risk-based cost functions change according to the operator attitude by about 522.44 $ for the ancillary parameter and scenario deviation equal to 10% and 20%, respectively.

An IGDT-based a low-carbon dispatch strategy of urban integrated energy system considering intermittent features of renewable energy

The current global energy crisis has prompted a significant rise in the use of low carbon units, making it a crucial research trend in the field of urban energy system, especially in terms of addressing the intermittency of renewable energy generation. This study proposes an integrated energy system planning model (UIES) for urban areas. In addition, to tackle the issue of carbon emissions, a reward and punishment ladder-type carbon transaction model is presented, which aims to control the level of carbon emissions within urban areas. To evaluate the performance of the UIES model, the authors develop an objective function that integrates the sum of equipment operation and maintenance costs, equipment daily acquisition costs, purchased energy costs, carbon trading costs, and integrated demand response (IDR) costs. The stochastic features of renewable energy sources are taken into account in the proposed approach, and an information gap decision theory (IGDT) based strategy is designed to quantify the uncertainty interval and intermittent fluctuations associated with renewable energy. This approach also aims to mitigate any negative effects and variations that may occur during the urban energy system's IDR process. Finally, the simulation tests to demonstrate that the average decrease in operating cost of both models based on IGDT strategy are 7.36 %, and the peak of electrical (EL), heating (HL) and cooling load (CL) are reduced by 20.45 %, 12.38 % and 5.02 % respectively. in conclusion, the energy service providers in urban areas can effectively manage renewable energy fluctuations by independently adjusting the operation sequence of equipment, and the economy and stability of the UIES can be effectively improved by the proposed dispatch strategy.

A risk‐averse framework for techno‐economic analysis and size optimization of an off‐grid hybrid energy system

AbstractIn the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES.

Mid-term scheduling and trading decisions for cascade hydropower stations considering multiple variable uncertainties

Cascade hydropower producers face two stages of risk when participating in medium and long-term market transactions: transaction risk during the bidding stage; and the operational risk during the scheduling and operation stage due to the uncertainty of runoff and market-clearing prices. Therefore, how to measure the above risks and make corresponding decisions has become an urgent problem for producers.This paper combines the real market structure and rules of a certain hydropower dominated market in Southwest China, and establishes a mid-term operation and trading decision-making method based on the Joint Information Gap Decision Theory (IGDT) and Prospect Theory. To address the main uncertainty variables that producers face in participating in transactions, this paper obtains the maximum fluctuation range of variables that satisfy the expected revenue in a robust model based on IGDT. Then, using Prospect Theory, a bidding strategy model that takes into account the psychological factors of producers is constructed within this range.To solve the nonlinear programming problem and address the accuracy issues caused by curve fitting during the solution process, a nonlinear programming combined with an improved stepwise optimization hybrid algorithm is employed.Using actual data from a hydropower grid in southwest China participating in the market as an example. The results indicate that the method provides the fluctuation range of runoff and market prices under different expected return targets, and can formulate reasonable bidding decisions and operation plans based on producers different risk preferences within this range.

A three-layer planning framework for regional integrated energy systems based on the quasi-quantum theory

With the rapid development in social informatization, more and more factors, such as regional economy, technological development, and people’s living needs, will affect the supply–demand relationship of regional integrated energy systems (RIES), which involve multiple energy forms. These factors turn the supply–demand relationship of an energy system into a nonlinear and time-varying chaotic system. This makes it difficult to predict and balance these relationships, which poses a huge challenge to the prediction, planning, operation, etc. Existing conventional methods attempt to quantify the prediction bias caused by various external environmental factors of energy systems and to weaken the uncertainty caused by multiple energy loads through random programming. However, uncertainty factors increase with the development of an energy system, thereby inreasing the requirements of such uncertainty quantification methods. Furthermore, in conventional methods, the prediction or planning decisions are often made by an observer while neglecting the impact of the observer’s decision-making behavior on prediction and planning. Therefore, this paper proposes a three-layer planning framework for to solve the above problems. This architecture includes quasi-quantum uncertainty periodic evaluation, stochastic planning based on information gap decision theory, and rolling planning based on model predictive control. First, we establish a quasi-quantum model of multi-energy system prediction error based on the quasi-quantum wave function model to qualitatively analyze prediction errors affected by uncertainty before and after planning. Simultaneously, combined with the evaluation model of the quasi-quantum potential energy function, a quasi-quantum uncertainty period evaluation model is proposed. Based on the minimum equivalent planning entropy, the planning cycle of multistage rolling planning is divided to minimize uncertainty. Second, the information gap decision theory is used to quantify the influence range of source and load uncertainty in the planning process and the multistage planning cycle is randomly planned. Then, the model predictive control method is used to control the actual error, and the planning strategy is changed in time to reduce the planning deviation caused by the prediction error. Finally, adopting a Beichen demonstration area in Tianjin, China, as a case study, the effectiveness of the proposed method in uncertainty analysis and long-term planning improvement is verified. The three-layer planning framework can improve the adaptability of the regional integrated energy system in the long-term planning process, and can timely adjust the planning scheme to cope with the impact of unpredictable uncertainties in the planning process.© 2017 Elsevier Inc. All rights reserved.

RETRACTED: Optimum operation of energy hub by considering renewable resources by considering risk tolerance and risk taking with Teaching–learning-based optimization

Amidst growing concerns over climate change, the conventional energy landscape faces critical challenges associated with fossil fuel usage. In response, this study explores alternative approaches by investigating an energy hub system leveraging wind turbine-generated power. This hub integrates seamlessly with electricity and natural gas grids, offering a comprehensive energy supply encompassing electricity, natural gas, and thermal loads. The research strategy encompasses a multi-pronged approach. A Time-of-Use (TOU) load management program optimizes the distribution of electrical and thermal loads. To address the unpredictability of wind speed, the information gap decision theory (IGDT) is introduced to manage risk and deviations effectively. The central objective involves optimizing the energy hub's operations, focusing on minimizing operational costs and emissions. To address this intricate challenge, the study employs the Teaching–Learning-Based Optimization (TLBO) method. This advanced technique concurrently optimizes cost and environmental impact, exemplifying its efficacy in the energy hub context. The research culminates in demonstrating the TLBO algorithm's swift convergence, highlighting its aptitude for addressing complex cost and emission optimization dynamics. In sum, this study offers innovative insights into sustainable energy systems, where economic and environmental considerations converge harmoniously.

Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed.

An improved IGDT approach for distributed generation hosting capacity evaluation in multi-feeders distribution system with soft open points

Unknown integrated locations caused by unpredictable consumers’ willingness and uncertainty in outputs induced by variable natural conditions are acknowledged challenges in the evaluation process of distributed generation (DG) hosting capacity (DGHC). For the former factor, this paper first proposes a novel assessment model to obtain the maximum feasible capacity no matter consumers in which locations have the possibility to stall DG. The decision variables of max operator and min operator are DG capacities and integrated locations respectively. Notably, unlike the previous works for one radial feeder, the interplay of DGHC in multi-feeders interconnected by tie lines and soft open points is also taken into consideration. After that, to combine uncertainties in integrated locations and outputs simultaneously, the above DGHC assessment model is further transformed into an improved information gap decision theory (IGDT) model with worst-case constraints for DG integrated locations. The specific solution procedure is also developed to solve this nonlinear IGDT model effectively. Case studies demonstrate that the evaluation results of DGHC based on this method always have the applicability and feasibility for any nodes in distribution system.

Information gap decision theory-based optimization of joint decision making for power producers participating in carbon and electricity markets

Carbon markets have been established in many countries and regions with the goal of promoting global carbon neutrality. The position of power producers as the dominant carbon emitters necessitates that they engage in both electricity and carbon markets. However, most studies have considered only short-term electricity markets and unrealistically static carbon markets, and the speculative behavior of power producers in the carbon market remains poorly considered. The present study addresses these issues by proposing an optimized joint decision model based on information gap decision theory to facilitate the participation of power producers in annual and monthly electricity markets, and monthly carbon markets, where uncertainties in the prices of electricity and carbon quotas, and the speculative behavior of power producers in the carbon market are explicitly considered to ensure that the revenues of market participants do not fall below a predetermined minimum acceptable value. The results of simulations based on the rules and actual market data obtained for electricity and carbon markets in a specific province of China demonstrate that the proposed model provides power producers with trading solutions to meet different expected revenue targets, and thereby assists them as much as possible in counteracting the risks to profit associated with fluctuations in electricity and carbon market prices.

Information Gap Decision Theory-Based Stochastic Optimization for Smart Microgrids with Multiple Transformers

Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park’s peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids.

Integrated energy system planning considering renewable energy uncertainties based on multi-scenario confidence gap decision

The problem of renewable energy uncertainties in the capacity planning of integrated energy system (IES) is prominent. To handle the multiple uncertainties, multi-scenario clustering analysis and classified confidence intervals of Gaussian mixture model (GMM) are combined, along with the robustness idea of information gap decision theory (IGDT), so a novel multi-scenario confidence gap decision theory (MCGDT) is proposed. Considering the comprehensive optimization objectives of maximizing exergy efficiency and minimizing annualized total cost, a robust capacity planning model for IES based on MCGDT is constructed to promote the cascade utilization of multiple energy and the economic performance of IES. Moreover, a new cross entropy-radar scanning differential evolution (CE-RSDE) algorithm is designed to improve solution efficiency and avoid prematurity in the optimization process. The simulation results of a typical case suggest that the proposed method leads to better capacity planning of IES, with up to 7.55% reduction of the annualized total cost and 14.03% increase in the exergy efficiency, while keeping strong robustness under the environment of multiple uncertainties.

Optimal scheduling of integrated energy systems with carbon and hydrogen storage based on IGDT

To deal with the uncertainties that the integrated energy system will encounter during operation, we propose an optimal scheduling strategy for the integrated energy system considering carbon and hydrogen storage. First, a comprehensive energy system containing a carbon capture system (CCS), power-to-gas technology (P2G), photothermal power station, and other equipment is established, and the combined operation of CCS and P2G is considered. Secondly, the carbon storage device and hydrogen storage device were respectively applied to the carbon capture system and P2G device, and the optimal scheduling model of the comprehensive energy system containing carbon storage and hydrogen storage was constructed. The operational constraints of the energy system will be integrated and the flexibility of the system will be improved. Finally, considering the dual uncertainties of the system source and load, a robust model and a chance model are constructed based on the Information Gap Decision Theory (IGDT). The economy of the real model and the influence of uncertainty on the whole system can be verified by close to real examples.

Determining optimal generator start-up sequence in bulk power system restoration considering uncertainties: A confidence gap decision theory based robust optimization approach

A reasonable start-up sequence of generators considering renewable energy generation integration can effectively improve the restoration efficiency of the power system concerned. The restoration strategy of generators is time-sensitive, however, the computation procedure of existing methods is not fast enough to meet the requirements of practical applications. In this paper, the generator start-up sequence optimization (GSSO) problem is addressed with the integration of wind-solar hybrid systems (WSSs) considered in bulk power systems. Specifically, a deterministic model for GSSO is formulated to maximize the total generation capability during the restoration procedure, in which fewer auxiliary variables are required to promote the computational efficiency. On this basis, in order to deal with the inherent and significant uncertainties of WSSs, a confidence gap decision theory (CGDT) based approach is proposed combining the advantages of stochastic optimization and the information gap decision theory based method while considering the worst-case realization of unknown probability distributions, so that a chance-constrained robust generator start-up solution is thereby attained. Moreover, the formulated CGDT based model is transformed into a mixed integer linear programming problem. Finally, the IEEE 39-bus power system and a modified version of an actual power system in China are employed to demonstrate the feasibility and efficiency of the proposed method.

Risk‐averse optimal operation of an on‐grid photovoltaic/battery/diesel generator hybrid energy system using information gap decision theory

AbstractThis paper focuses on risk‐averse‐based optimal operation of a grid‐connected hybrid energy system (HES) composed of photovoltaic (PV), diesel generator, and battery storage system (BSS). For this goal, information gap decision theory (IGDT) is used to model load demand uncertainty. The aim of the optimal operation is to minimize cost of PV/diesel/BSS by optimal determination of the power purchased from the electricity grid. Since in the risk‐averse strategy, load demand has an undesirable impact on the objective function, the decision maker attempts to maximize the uncertainty radius in a way that any deviation of the uncertain parameter leads to an objective function value which is not worse than the critical value. Over the case studies (considering different radiations), simulation results indicate that in the risk‐neutral strategy, at high, medium, and low radiations, the operation cost is 28.88, 36.10, and 42.63$, respectively. In the risk‐averse strategy, when the radiation is high, by increase of the deviation factor from 0.1 to 0.25, the optimal uncertainty radius increases from 6.98% to 15.72% (increase of around 125%) and the operation cost increases from 31.768 to 36.101$. When the radiation is low, by increase of the deviation factor from 0.1 to 0.25, the uncertainty radius increases from 8.64% to 16.9% (increase of around 96%) and the operation cost increases from 46.895 to 53.291$.

Optimal bidding strategy of multi-carrier systems in electricity markets using information gap decision theory

This paper proposes a novel optimization framework based on information gap decision theory (IGDT) for strategic participation of multi-carrier systems (MCS) in the electricity market under price uncertainty. The proposed framework helps the operator of MCS to take advantage of flexible generation units such as combined heat and power (CHP) systems, boilers, and battery energy storage systems (BESS) and participate in the electricity market considering the strategies presented by IGDT against the uncertainty in the market clearing price. The proposed optimization framework is developed as a bilevel problem, in which the risk-based bidding strategy of the MCS operator is modeled in the upper-level problem, and the energy market is cleared by the bulk power system operator in the lower-level problem. Karush–Kuhn–Tucker conditions are used to transform the bilevel problem into a single-level optimization problem. Duality theory is later used to approximate the product of power and market clearing price in the MCS operation problem, and the non-linearity enforced by the implementation of IGDT is handled by a linearization method called Triangle method. The studied optimization framework is modeled as a single-level mixed-integer linear programming problem, and CPLEX is used to solve it. The simulations are carried out on the modified IEEE 6-bus transmission network, and the results are analyzed to verify the economic and computation efficiency of the studied model. According to the simulation results, by taking a risk-averse bidding strategy provided by the IGDT, the operation cost of MCS increases 16.6% in order to increase the robustness level of MCS up to 36%, indicating that MCS becomes robust against 36% of the increase in the electricity market price, compared to the case with a risk-neutral bidding strategy being taken.