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The increasing uncertainty and volatility of net load caused by the high penetration of renewable energy leads to higher demand tariffs for industrial park and potentially impacts their economic benefits. To tackle these issues, this paper develops a novel business mode to enable rental energy storage sharing among multiple users within an industrial park, and propose a robust optimization and demand defense-based iterative bi-layer planning framework. The upper layer focuses on the maximization of the investment profitability of shared rental energy storage by developing a robust information gap decision theory optimization. Meanwhile, the lower layer is dedicated to enhancing the demand defense ability of shared rental energy storage in real-time operation through the formulation of a distributed model predictive control. After that, the synchronous alternating direction multiplier method with consistency theory is derived for solving the distributed optimization. Numerical results demonstrate that the proposed shared rental energy storage is 6.391% and 7.714% more economical than shared and self-built energy storage, respectively. Moreover, the iterative bi-layer planning enables flexible energy storage capacity configuration, reduces the impact of net load uncertainty, improves the ability of demand defense, and enhances the system's overall economy. • A novel business mode to enable rental energy storage sharing is proposed. • An iterative bi-layer optimization for shared rental energy storage is developed. • The model leverages both robust optimization and demand defense. • A fully distributed control in conjunction with consistency theory is derived. [ABSTRACT FROM AUTHOR]

Hydrogen energy has the advantages of being clean and carbon-free, convenient storage, and easy conversion to electrical energy. The use of renewable energy electrolysis for hydrogen production is an important solution to promote clean energy utilization and decarburization in the power and transportation industries. However, hydrogen has a low hydrogen storage density and is prone to explosion. The high cost and risk of hydrogen transportation pose challenges to the promotion and utilization of hydrogen. To address these difficulties, this paper proposes a day-ahead dispatch of electricity-hydrogen systems (EHS) under the solid-state transportation mode of hydrogen energy. Firstly, based on the Van't Hoff equation, the relationship between gas pressure and the reaction temperature is established during the gas-solid conversion process, and a hydrogen energy solid-state transport model based on a magnesium-based hydrogen transport vehicle (MHTV) is proposed. Secondly, a renewable energy uncertainty set is established based on the information-gap decision theory (IGDT) envelope constraint for addressing the uncertainty of renewable energy and a day-ahead bi-level dispatch model of the EHS is constructed based on the solid-state transportation mode of hydrogen energy. Finally, the IGDT approach considering fuzzy variables (FV-IGDT) is presented by using a fuzzy variable membership function, and the proposed bi-level model is transformed into a single-level model for a solution based on this approach. The effectiveness of the proposed model and solution method is verified through simulation on an EHS consisting of a modified IEEE-118 power system, 2 hydrogen production stations (HPS), and 10 hydrogen refueling stations (HRS). • Hydrogen pressure & temperature during gas-solid conversion established by Van't Hoff equation. • Hydrogen energy solid-state transport model based MHTV is proposed using magnesium. • MHTV hydrogen transportation time, energy balance, time-coupling constraints are considered. • FV-IGDT model is proposed to address issue of traditional IGDT models unable to solve optimal value. • Optimize fluctuation range of uncertain variables in RSOS and RARS models and optimal values. [ABSTRACT FROM AUTHOR]

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. • Combination of information gap decision theory and particle swarm optimization for DLMP calculation. • Achieving a delicate balance between maximizing DG owner profits and ensuring overall system optimality. • Presenting an accurate and realistic solution for DLMP calculation. [ABSTRACT FROM AUTHOR]

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. • Proposing a novel IGDT-based model for strategic participation of MCS in power market. • A bilevel model for risk-based bidding strategy of the MCS and clearing energy market. • Considering strategies of IGDT against the uncertainty in the market clearing price. • Approximating product of power and market clearing price in MCS operation problem. • Analysis of economic and computation efficiency of the proposed model by simulations. [ABSTRACT FROM AUTHOR]

The accumulation of many production units with small capacities and transforming them into a larger entity will make them visible in electricity market. Renewable based virtual power plant (VPP) in this paper is a wide energy management system that incorporates probabilistic wind and solar units, non-renewable Distributed Generation (DG) units, and dispatchable loads. In an electricity market, a VPP optimizes its operating schedules in order to increase its economic efficiency. However, market uncertainties may influence the VPP's profit. In this paper, modelling the uncertainties is implemented by the proposed Information Gap Decision Theory (IGDT). The mentioned scheduling problem is formulated in three operation modes: risk-neutral, risk-averse and risk-seeker. The risk-neutral mode focuses on optimizing the VPP in the day-ahead market. In the risk-averse mode, the robustness function is used under low market prices. Moreover, in the risk seeker mode, an opportunity function is used under higher market prices towards higher profit results. The proposed model allows the VPP to decide on the scheduling of its components and the optimal bids to the day-ahead market. Another purpose is to investigate the role of the renewable-based VPP in minimizing emission and maximizing profit in a two-objective way. The IEEE 18-bus test system is utilized to simulate the proposed problem and analyse the results. The performance of the proposed problem is approved using different scenarios. Simulation results justify the advantages and necessities of the proposed problem. • The renewable-based VPP includes conventional, wind and solar units and dispatchable loads. • Modelling the uncertainties is implemented by the proposed Information Gap Decision Theory. • The mentioned scheduling problem is formulated in three operation modes. • The proposed model allows the VPP to decide on scheduling of its DERs. • Another goal is to solve the problem in a two-objective way. [ABSTRACT FROM AUTHOR]

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very time-consuming, and is also not able to provide a level of cost assurance, the deterministic method is not able to determine the effect of uncertainties on system planning, but the IGDT can easily determine the cost of the system based on changes in uncertainty parameters. Also, planning based on the IGDT has resulted in the achievement of a robust hybrid system against forecasting errors caused by uncertainties. [ABSTRACT FROM AUTHOR]

With the advent of multi-carrier energy systems in the context of local energy markets, e.g., thermal market, virtual energy hub plants have become more prominent in many countries to achieve sustainable energy supply networks. The former researches on techno-economic operation of virtual energy hub plants did not consider the promoted power-to-X (P2X) conversion facilities alongside hydrogen market to assess the energy efficiency, greenhouse gas emissions, and economic issues in an integrated manner. To this end, this paper presents a risk-averse multi-objective method to tackle the self-scheduling problem of the virtual energy hub plant and address the mentioned issues. The considered virtual energy hub plant is operated based on the different energy carriers, i.e., electricity, heating, cooling, and hydrogen, P2X units, electric vehicles, and developed demand response (DR) strategy. The virtual energy hub plant operator tries to maximize its revenue by participating in the local energy markets and minimizing CO2 emission rates considering different uncertain sources. The combined heat and power (CHP) unit and DR strategy are utilized as practical solutions for the instability of renewable power output as well as exploiting opportunities in various local energy markets. To model the proposed strategy, a non-probabilistic information gap decision theory (IGDT) is developed. The presented model is applied to a sample test case, and numerical results confirm that the profit of the virtual energy hub plant is increased by up to 17.54%, and the CO2 emission rate is reduced by up to 10.24% considering the up-to-date energy storage systems, developed DR strategy, and P2X units. • Trading of the virtual energy hub plant in the electricity, hydrogen, and thermal energy markets. • The flexibility level of the virtual energy hub plants is improved by considering the impact of the hydrogen market. • The maximum profit for the virtual energy hub plant as well as minimum carbon emission rate are achieved. • The eco-friendly energy conversion facilities development such as P2X, P2H, P2C and P2HY. [ABSTRACT FROM AUTHOR]

Energy distribution network operation will be a major problem in the systems shortly because most changes will emerge in this domain. The presence of energy hubs, natural gas grids and dispersed generation resources at the distribution network's moderate- and low-voltage levels is the first challenge facing the energy distribution network operator. When PHEV vehicles are included in the energy hub, batteries can act as a general storage system and increase the possible integration of RES within power system networks. For the optimal operation of an energy hub that involves RES, PHEV, electrolyzer, fuel cell vehicles, boiler, hydrogen tank, rectifier, a heat storage system and inverter, a new model has been proposed in this paper. A new model has been developed for estimating consumption-related uncertainty of PHEVs during trips using information gap decision-making theory IGDT under risk aversion and risk-taking strategies. Additionally, a model for the uncertainty of RES is put out based on scenario development and reduction. A unique solution based on routing is presented since tackling the problem mentioned above might be error-prone mathematically. In this model, the possibility of entrapment in local optimum is reduced by increasing local and global searches. Finally, the proposed method is evaluated on a sample system and in different scenarios. Consequently, the proposed method maximizes target operation under risk-free and risk-averse strategies while minimizing target performance under risk-taking strategies. [Display omitted] • Application of IGDT, risk-aversion and risk-taking strategies. • Modelling the uncertainty related to the power consumption of PHEVs during travel. • Providing incentives to PHEV owners in the form of electricity discount rates. • Integration of FCVs in the energy hub since FCVs consume hydrogen based on new algorithm. • Probabilistic modelling of renewable sources, consumed load, & analyzing the proposed method. [ABSTRACT FROM AUTHOR]

The proliferation of green mobility will bring multiple benefits to the society; however, it may be counterproductive for power systems if its integration is not properly planned. In this context, Intelligent Parking Lots have emerged as a valuable paradigm for integration of electric vehicles into energy systems. This framework consists of a set of vehicles that are managed as a whole and makes possible to exploit them as large storage facilities through their vehicle-to-grid capability. This particular feature may be significantly advantageous for energy communities since they can exploit parking lots as collective storage systems. In this paper, a two-stage optimal scheduling framework has been developed for optimal scheduling of energy communities. The proposal uses a stochastic representation of the state-of-charge of the lots with the end of accounting for random behaviour of uncertainties. On the other hand, the uncertainty of the upstream energy market is dealt with Information Gap Decision theory, resulting in an original hybridization that allows to adopt a risk-averse strategy by the operator. The optimization problem is formulated as a Mixed-Integer Linear programming model that can be efficiently solved by average solvers. A case study is performed to validate the new proposal and analyse the role of Intelligent Parking Lots in energy communities. The results evidence the advantages that electric vehicles may bring to communities if they are optimally exploited, highlighting their capability to enhance the efficiency and economy of the system. • A novel scheduling tool for energy communities encompassing IPLs is developed. • A stochastic model is proposed for SOC estimation in IPLs. • Upstream energy pricing is modelled using IGDT. • Results reveal the importance of optimally sizing IPLs. • Selling energy to the grid mainly determine the monetary balance of the community. [ABSTRACT FROM AUTHOR]

The accommodation of wind energy is restricted due to the heat-led operation mode of CHP units. Comprehensive utilization of multi-energy coordinated supply and integration of wind energy, is deemed as an efficient solution for improving energy conservation and operational flexibility. Therefore, a novel hybrid stochastic/interval optimization for an integrated heating and power system (IHPS) day-ahead scheduling is proposed in this paper. Considering the bilateral uncertainties that exist on both sides of supply and demand, the scenario-based stochastic analyze is adopted to generate a set of possible scenarios for describing uncertainties of electrical and thermal loads. Also, the information gap decision theory (IGDT) is proposed to deal with the severe uncertainty caused by high wind power penetration. For the operation of district heating network (DHN), temperature dynamics and transmission delay are studied to exploit the heating system as a virtual storage for managing the dispatch of wind power. To further improve the system economic, the concept of price-based integrated demand response (IDR) under the background of multi-energy coupling is also introduced and the characteristics of energy substitution and load timing transfer are modeled. Simulation results show that the benefits of the proposed method for enhancing the system operation flexibility. • A node method is used to describe dynamic characteristics of the DHN. • Load timing transfer and energy substitution are modeled as a price-based DR. • A hybrid stochastic/interval robust optimization strategy for IHPS is proposed. [ABSTRACT FROM AUTHOR]

With the emergence of smart appliances and communication infrastructures, Home Energy Management Systems have gained importance to help home users to reduce their electricity bills. A Home Energy Management System is a tool able to optimally coordinate the different home assets such as controllable appliances, onsite generators and storage facilities, among others. Such kind of tools has become more complex with the appearance of dynamic pricing tariffs and novel appliances such as electric vehicles. In this context, scheduling tools must attain a high level of robustness against uncertainties as well as being able to consider the particular behaviour of unpredictable energy pricing and vehicle routines, while some complementary objectives like thermal comfort are not ignored. This paper addresses this issue by developing a novel hybrid robust-multi objective home energy management approach. The novel proposal is based on information gap decision theory and lexicographic optimization, which are combined in an original way to attain a scheduling plan immune against the negative effect of uncertainties, while the economy and comfort of the building are jointly considered. The developed mathematical formulation is Mixed Integer Linear Programming, employing advanced linearization techniques to overcome the problems arisen from nonlinear models. Its particular integer-linear structure makes the developed optimization problem easily tractable by conventional solvers and average machines. Also, further capabilities are explored such as the possibility of selling energy to the grid and the vehicle-to-home ability of electric vehicles. Extensive simulations are performed on a benchmark prosumer environment considering real time pricing and time-of-use tariffs. The result serves to prove that the developed methodology is able to deal with uncertainties whereas different objective functions are jointly accounted. • Home Energy Management Systems have gained importance to help home users. • A novel hybrid robust-multi objective home energy management approach. • The novel proposal is based on information gap decision theory and lexicographic optimization. • The developed mathematical formulation is Mixed Integer Linear Programming. • The developed methodology is able to deal with uncertainties. [ABSTRACT FROM AUTHOR]

Introducing new technologies in co-generation and tri-generation systems has led to a rapid growth toward the energy hubs (EHs) as an effective way for coupling among various energy types. On the other hand, the energy systems have usually been exposed to uncertain environments due to the presence of renewable energy sources (RESs) and interaction with the electricity markets. Hence, this paper develops a novel optimization framework based on a hybrid information gap decision theory (IGDT) and robust optimization (RO) to handle the optimal self-scheduling of the EH within a medium-term horizon for large consumers. The proposed mixed-integer linear programming (MILP) framework aims to capture the advantages of both the IGDT and RO techniques in dealing with the complicated binary variables and achieving the worst-case realization arisen from wind turbine generation and day-ahead (DA) electricity market uncertainties. The RO optimization approach is presented to model the DA electricity price uncertainty while the uncertainty related to the wind turbine generations is taken into account by the IGDT. Numerical results validate the capability of the model facing uncertainties. The amount of total operation cost of the EH increases by 8.6 % taking into account the worst-case realization of uncertainties through the proposed hybrid IGDT-RO compared to the case considering perfect information. Besides, the results reveal that optimal decisions can be taken by the operator using the proposed hybrid IGDT-RO model. • Proposing a linear hybrid IGDT-robust optimization model for robust operation of EH. • Considering the medium-term contracts in the hybrid IGDT-robust optimization model. • Integrating the robust model with the P2G technology in a medium-term horizon. • Presenting an uncertainty budget model to control the different volumes of uncertainties. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) are units wherein multiple energy carriers can be converted, stored and conditioned to simultaneously supply different energy demands. In this paper, a new model is proposed for unit commitment in renewable EHs with electric, thermal and cooling demands, different storage systems, combined heat and power (CHP) unit, boiler, electric chiller, absorption chiller, PV module, wind turbine and battery charging station (BCS). Using information gap decision theory (IGDT), day-ahead EH scheduling is done from risk-neutral, risk-averse and risk-seeking perspectives, considering the uncertainties of electric demands, BCS demands, heat demands, cooling demands, PV and wind power and electricity prices. Comprehensive models are used for storage systems considering their degradation, charging loss, discharging loss and storage loss; the ramp-up and ramp-down rate limits, start-up and shut-down costs of CHP, boiler and cooling components are considered. The effect of risk as well as effect of critical cost deviation factor and target cost deviation factor on EH operation cost and schedule of EH components is investigated. The findings indicate that the sensitivity of EH operation cost may be very different with respect to different sets of uncertain input data. The findings also show the significant effect of risk-awareness on schedule of EH components and its operation cost. [Display omitted] • Risk-aware scheduling is done for combined cooling, heat and power energy hubs. • Uncertainties of demands, PV and wind power and electricity price are considered. • Effect of risk awareness on EH scheduling and operation cost is investigated. • Effect of critical/target cost deviation factor on EH operation is investigated. • Ramp-up/down limits, start-up/shut-down costs of cooling components are considered. [ABSTRACT FROM AUTHOR]