Risk-averse Strategy Research Articles

Exploitation of Microgrid Flexibility in Distribution System Hosting Prosumers

Increasing the penetration of renewables on prosumers' side brings about operational challenges in the distribution grid due to their variable and uncertain behavior. In fact, these resources have increased the distribution grid net load fluctuation during recent years. In this article, the flexibility-oriented stochastic scheduling of a microgrid is suggested to capture the net load variability at the distribution grid level. In this scheduling, the flexibility limits are set to manage the net load fluctuation at a desirable level for the main grid operator. The uncertainties of load and renewables are considered, and their uncertainties are under control by the risk-averse strategy. Moreover, multiperiod islanding constraints are added to the problem, preparing the microgrid for a resilient response to disturbances. The model is examined on a typical distribution feeder consisting of prosumers and a microgrid. The numerical results are compared for both flexibility-oriented and traditional scheduling of a microgrid at the distribution level. The proposed model reduces the net load ramping of the distribution grid using an efficient dispatch of resources in the microgrid. A sensitivity analysis is also carried out to show the effectiveness of the model.

Optimal operation strategies of multi-energy systems integrated with liquid air energy storage using information gap decision theory

In this paper, a framework of multi-energy system (MES) integrating with a liquid air energy storage (LAES) system was proposed. LAES, where liquid air works as an energy storage media, is a powerful and eco-friendly technology for storing renewable energy resources and reducing grid curtailment. Considering the characteristics of LAES (i.e. cold and heat circulation), the incorporation of LAES system into the Combined Cooling, Heating and Power system can achieve integrated use of energy and effectively save energy. Moreover, the prices of electricity will affect the overall cost of the MES. In other words, the decision-makers of the MES need to consider the uncertainty of electricity prices when making power dispatching decisions. To model the uncertainty of electricity prices, the information gap decision theory method was used to study power dispatching strategies of the MES. Three different strategies were proposed, including risk-neutral, risk-averse and risk-taker. In addition, demand response algorithms were used to study load transfer strategies. The results show that the demand responses of the three strategies are effective in terms of load transfer and cost saving. The total operation cost in the risk-neutral strategy with demand response can be 6.82% less than that without demand response; In the risk-taker strategy with demand response, the allowable grid electricity price is reduced by 25.24% when the opportunity cost drops by $8,000, and 23.32% without demand response. With additional robustness cost, the acceptable price change ratio using demand response is 21.91% in the risk-averse strategy, and 20.04% without demand response.

A RA-IGDT model for stochastic optimal operation of a microgrid based on energy hub including cooling and thermal energy storages

Energy Hub (EH) is known as a complex system capable of transferring, conversion, and saving various energies in a Microgrid (MG). An important issue for investors is taking the advantage of EH for optimal management of all energy carriers, particularly in anticipating energy price. In this paper, a model for EH is considered for the purpose of the optimal operation of MG with multiple energy carrier infrastructures for day-ahead. The goal of optimization is to minimize operational and environmental costs subject to numerous technical constraints. The proposed EH manages dispatchable generation, i.e. Combined Cooling, Heat and Power (CCHP) as well as non-dispatchable generations, i.e., Wind Turbine (WT) and Photovoltaic (PV). It considers an Ice Storage Conditioner (ISC) along with a Thermal Energy Storage System (TESS) as the Energy Storage System (ESS). In particular, the effects of Solar-Powered Compressed Air Energy Storage (SPCAES) as a novel ESS are studied on the performance and efficiency of the EH operational and environmental costs. A bi-level stochastic optimization problem based on information gap decision theory (IGDT) with risk averse (RA) strategy is considered to save the Microgrid operator (MGO) from the risks of information gap between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is reorganized into a single level problem obtained by Karush-Kuhn-Tucker method. As uncertainty variables compete to expand their enveloped-bounds, the augmented ε-constraint method is employed to address the multifaceted RA-IGDT-based stochastic optimization problem proposed in the study. Implementation of the proposed framework on the typical EH shows the efficacy of the ESSs to reduce the operation costs as well as the emissions in the day-ahead energy management.

A risk-averse decision based on IGDT/stochastic approach for smart distribution network operation under extreme uncertainties

Renewable generation as well as electrical demand uncertainties cause significant technical challenges in addition to associated financial consequences in smart distribution networks (SDNs), particularly in the electricity markets, which are restructured and are featured by smart grids. In this paper, a risk-averse-strategy-based decision making tool is proposed to help the smart distribution network operator (SDNO) in day-ahead operational practices including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The proposed tool aims to reduce the consumers’ electricity prices as well as to optimize the financial transactions with the energy market, reliability of distributed generation (DG), electricity storage system (ESS) dispatch, and planning interruptible electrical demands in order to secure the specified revenue targets for SDNO by means of the risk-averse strategy. A bi-level stochastic optimization problem based on information gap decision theory (IGDT) is considered to preserve the SDNO from the risks of information gap between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is applied to a single-level problem obtained by Karush–Kuhn–Tucker method. As uncertainty variables compete to expand their enveloped-bounds, the enhanced ε -constraint method is employed to address the multifaceted IGDT-based stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the proposed model are evaluated on an IEEE 33-bus SDN.

Ventral Pallidum GABA Neurons Mediate Motivation Underlying Risky Choice.

Pursuing rewards while avoiding danger is an essential function of any nervous system. Here, we examine a new mechanism helping rats negotiate the balance between risk and reward when making high-stakes decisions. Specifically, we focus on GABA neurons within an emerging mesolimbic circuit nexus: the ventral pallidum (VP). These neurons play a distinct role from other VP neurons in simple motivated behaviors in mice, but their role in more complex motivated behaviors is unknown. Here, we interrogate the behavioral functions of VPGABA neurons in male and female transgenic GAD1:Cre rats (and WT littermates), using a reversible chemogenetic inhibition approach. Using a behavioral assay of risky decision-making, and of the food-seeking and shock-avoidance components of this task, we show that engaging inhibitory Gi/o signaling specifically in VPGABA neurons suppresses motivation to pursue highly salient palatable foods, and possibly also motivation to avoid being shocked. In contrast, inhibiting these neurons did not affect seeking of low-value food, free consumption of palatable food, or unconditioned affective responses to shock. Accordingly, when rats considered whether to pursue food despite potential for shock in a risky decision-making task, inhibiting VPGABA neurons caused them to more readily select a small but safe reward over a large but dangerous one, an effect not seen in the absence of shock threat. Together, results indicate that VPGABA neurons are critical for high-stakes adaptive responding that is necessary for survival, but which may also malfunction in psychiatric disorders.SIGNIFICANCE STATEMENT In a dynamic world, it is essential to implement appropriate behaviors under circumstances involving rewards, threats, or both. Here, we demonstrate a crucial role for VPGABA neurons in high-stakes motivated behavior of several types. We show that this VPGABA role in motivation impacts decision-making, as inhibiting these neurons yields a conservative, risk-averse strategy not seen when the task is performed without threat of shock. These new roles for VPGABA neurons in behavior may inform future strategies for treating addiction, and other disorders of maladaptive decision-making.

Risk-based bidding and offering strategies of the compressed air energy storage using downside risk constraints

The compressed air energy storage (CAES) can be participated independently in the power markets to buy and sell the electricity. Therefore, the electricity price’s uncertainty is a critical challenge for CAES operators to contribute in the day-ahead market. In this paper, stochastic optimization is modeled for a CAES to model the uncertain parameters and obtain the bid-offer curves to contribute to the energy markets. The risk-based bid-offer curves, including the risk-neutral and risk-averse strategies, are derived from the new risk-measure called downside risk constraints (DRC) method. The DRC method is used along with the stochastic problems to manage the uncertain parameters’ imposed risks. The proposed DRC’s main advantage is introducing a scenario independent strategy in the stochastic problems with equal risk over all scenarios. In other words, by using the DRC in the stochastic problems, the CAES operator can obtain a strategy that has the same profit in all scenarios. As represents the results, the expected profit of the stochastic problem is $ 9585. By implementing the DRC, the profit of the proposed strategy by DRC is $ 8845, which shows a 7.2% fall in the expected profit while the risk-in-profit is reduced by 100%.

A novel techno-economic risk-averse strategy for optimal scheduling of renewable-based industrial microgrid

Nowadays, increasing concerns regarding the harmful effects of fossil fuel-based energy production systems have accelerated the decarbonization efforts particularly in equipping the electric power grid with a high level of renewable energy sources. In deregulated energy networks with a high share of renewables, adopting an effective strategy for appropriately dealing with intermittences of stochastic producers is necessary to provide a continuous and reliable energy supply. This paper proposes a novel techno-economic risk-averse strategy for optimal scheduling of the industrial microgrid considering both the technical and economical objectives. The robustness function of the information gap decision theory method is exerted for developing the risk-averse strategy aiming to properly manage the fluctuations of uncertain parameters in the system. The demand-side energy management is carried out by developing the price and load response schemes as the demand response programs. The industrial microgrid equipped with renewable energy sources and energy storage systems is intended as a real case study for examining the effectiveness of the proposed strategy. The problem is studied under two models, which Model I assesses the scheduling of the industrial microgrid without intending the stochastic behaviors of uncertain parameters while Model II models uncertainties based on the proposed strategy. The results indicated the effectiveness of the proposed strategy in realizing robustness condition for the renewable-based industrial microgrid to supply reliable and continuous energy in the deregulated environment.

Dynamic optimal portfolio choice under time-varying risk aversion

In this paper, we empirically analyze the possible advantages of modelling a time-varying risk aversion that best fits investors’ behavior in the context of the optimal portfolio choice. We build optimal dynamic portfolios by focusing on the estimation of a time-varying relative risk aversion parameter (RRA). Conditional univariate and multivariate models, such as GARCH, GARCH-M and DCC-GARCH, for modelling the optimal portfolio choice and the RRA parameter are implemented. As a model validation tool, the realized performance and downside risk exposure of these portfolios one month ahead is compared to that resulting from implementing a constant risk aversion parameter. The Ledoit and Wolf (2008) test provides robustness to our results and reveals the average outperformance of the dynamic risk aversion strategy over others as the constant risk aversion or the passive management strategies.

Coordinated operation of coupled transportation and power distribution systems considering stochastic routing behaviour of electric vehicles and prediction error of travel demand

The popularisation of electric vehicles (EVs) and the development of dynamic wireless charging technology have created an emerging trend of transportation electrification, which strengthens the coupling between electrified transportation network (ETN) and power distribution network (PDN). Meanwhile, the stochastic routing behaviour of EVs and prediction error of traffic demand pose a severe challenge to the coordinated ETN-PDN operation problem. This paper proposes a new hybrid optimisation method using stochastic user equilibrium (SUE)/information gap decision theory (IGDT) to study the impact of the unavoidable uncertainties on the coordinated ETN-PDN operation, which consists of the following two stages. In the first stage, a collaborative optimisation model based on SUE and Dist-Flow equations is established to deal with the stochastic EV routing behaviour. Built upon this model, the second stage continues to consider the traffic demand prediction error and establish a risk decision model using IGDT. The proposed model can provide proper road congestion tolls and local generator production schedules to lead to a minimum expected socio-economic cost. Also, two different coordinated operation strategies, that is, risk-seeker and risk-averse strategies, are provided to deal with the uncertainties. Case studies are carried out to demonstrate the effectiveness of the hybrid SUE/IGDT optimisation method.

Two-stage robust energy management of a hybrid charging station integrated with the photovoltaic system

The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.

A new approach for optimal allocation of photovoltaic and wind clean energy resources in distribution networks with reconfiguration considering uncertainty based on info-gap decision theory with risk aversion strategy

In this paper, a new approach for the optimal and simultaneous allocation of clean and renewable energy resources as photovoltaic panels and wind turbines and reconfiguration in radial distribution networks is presented with the aim of minimizing the cost of power losses and increasing of reliability considering generation and load the uncertainty using a new approach named information gap decision theory (IGDT) with risk aversion strategy. Decision variables include the maximum uncertainty radius of generation and load, location and size of renewable resources as well as opened network lines that are determined by satisfying the operating and radiality constraints using improved salp swarm algorithm (ISSA) based on differential evolutionary (DE) operators. The problem is implemented as deterministic and IGDT-based methods on 33 and 69 bus networks. The results indicate that the scenario of wind turbine allocation and reconfiguration in the networks simultaneously is the best scenario with the lowest cost of losses and reliability and reconfiguration only scenario is the weakest scenario with the highest cost in the deterministic and the IGDT methods. The results also showed load increasing and clean production decreasing equal to 7.58% and 34.57% for 33 bus and 4.66% and 39.88% for 69 bus network using IGDT based risk aversion for 20% uncertainty budget. The results cleared that in IGDT, the percentage of changes in the uncertain parameters is clearly and simply obtained and the network operator is able to make robust and risk aversion decision according to existing the uncertainties unlike random methods based the Monte Carlo Simulation that the results depend on the definition of the large samples.

A hybrid approach based on IGDT‐MOCMA‐ES method for optimal operation of smart distribution network under severe uncertainties

Uncertainties involved in renewable generation and electrical demand pose significant technical challenges with the concomitant financial consequences in smart distribution networks (SDNs), particularly in the current electricity market, which is restructured and features smart grids. The present paper introduces a decision-making tool based on a risk-averse strategy to help with the smart distribution network operator (SDNO) in day-ahead operational practices, including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR). The tool is meant to reduce electricity prices presented to the electrical consumers and to optimize financial transactions with the energy market, distributed generation (DG) reliability, electricity storage system (ESS) dispatch, and planning interruptible electrical demands to secure specified revenue targets for SDNO with the risk-averse strategy. A bi-level stochastic optimization problem based on Information gap decision theory (IGDT) is considered to keep the SDNO from risks inherent in the information gap present between the predicted and actual uncertainty variables. The bi-level stochastic optimization problem is reorganized into a single-level problem obtained by Karush-Kuhn-Tucker method. As uncertainty variables compete to expand their enveloped-bounds, multi-objective covariance matrix adaptation-evolution strategy (MOCMA-ES) is employed to address the multifaceted IGDT-based stochastic optimization problem proposed in the study. Finally, the efficiency and efficacy of the suggested model are appraised on an IEEE 33-bus SDN. Simulation results show that optimal UC with both DFR and demand response program increases the total revenue by 8.1% compared to optimal operation without them.

Maize-cowpea intercropping as an ecological intensification option for low input systems in sub-humid Zimbabwe: Productivity, biological N2-fixation and grain mineral content

Poor soil fertility and erratic rainfall constrain crop production in rain-fed smallholder farming systems in sub-Saharan Africa. Integration of drought tolerant and N2-fixing crops into maize-based cropping systems is a risk-averse strategy that also improves nitrogen cycling. A field experiment was carried out during the 2017/18 and 2018/19 cropping seasons in Goromonzi district in Zimbabwe. The trials were established on 14 farms, and on two field types (homefields and outfields), with eight treatments from a combination of cropping systems (maize and cowpea monocrops or maize/cowpea intercrops) and with or without nitrogen fertilizer (+N; -N). The trials were implemented on the same field plots for the two consecutive seasons. An improved cowpea variety and a landrace were used. The objectives were to determine 1) the productivity of the different cropping systems under variable soil fertility conditions, 2) N2-fixation of the two cowpea types when planted as monocrops or intercrops, and 3) mineral composition of maize and cowpea grains from intercrops and sole crops. Contrary to expected results, soil properties were not significantly different (P > 0.05) between field types. The land equivalent ratios (LER) were >1 for both seasons, implying improved land productivity under intercropping. Intercropping significantly reduced cowpea nodulation and active nodules, but not the total nodule weight, resulting in similar proportion of nitrogen derived from the atmosphere (%Ndfa) for cowpea grown as monocrop or in intercropping with maize. However, the total amount of fixed nitrogen was reduced in intercropping systems due to the smaller cowpea biomass compared to monocropping. Maize and cowpea grain mineral contents (Fe, Zn, Mn, Cu, Ca, Mg, P, K) were significantly affected by the cropping season only. We showed that intercropping maize with cowpea generally increases system productivity, in addition to substantial amounts of nitrogen being added to the system through N2-fixation. However, intercropping was not an agronomic biofortification option in these nutrient-depleted soils. Finally, annual variation in grain mineral quality can be larger than the annual variation in grain yield, potentially posing serious challenges to human nutrition.

The Road Not Taken: How Early Landscape Learning and Adoption of a Risk-Averse Strategy Influenced Paleoindian Travel Route Decision Making in the Upper Ohio Valley

To evaluate a model of the travel-route selection process for upper Ohio Valley Paleoindian foragers (13,500–11,400 cal BP), this study investigates archaeological data through the theoretical framework of landscape learning and risk-sensitive analysis. Following initial trail placement adjacent to a highly visible escarpment landform, Paleoindians adopted a risk-averse strategy to minimize travel outcome variability when wayfaring between Sandy Springs, a significant Ohio River Paleoindian site, and Upper Mercer–Vanport chert quarries of east-central Ohio. Although a least-cost analysis indicates an optimal route through the lower Scioto Valley, archaeological evidence for this path is lacking. Geomorphic and archaeological data further suggest that site absence in the lower Scioto Valley is not entirely due to sampling bias. Instead, evidence indicates that Paleoindians preferred travel within the Ohio Brush Creek–Baker's Fork valley despite its longer path distance through more rugged, constricted terrain. Potential travel through the lower Scioto Valley hypothesizes high outcome variability due to the stochastic nature of the late Pleistocene hydroregime. In this case, perceived outcome variability appears more influential in determining travel-route decisions among Paleoindians than direct efforts to reduce energy and time allocation.

Risk-constrained self-scheduling of a hybrid power plant considering interval-based intraday demand response exchange market prices

Hybrid power plants (HPPs) integrating dispatchable and non-dispatchable generation are gaining attention by generation companies due to their increased flexibility in the operation of the power system. In this paper, an offering and bidding framework for an HPP consisting wind, photovoltaic (PV), compressed air energy storage (CAES), battery energy storage (BES), and thermal units in day-ahead (DA) and intraday markets is presented. Moreover, the interaction between the HPP and demand response providers (DRPs) through the intraday demand response exchange (IDREX) market is incorporated into the proposed model. The existing uncertainties such as DA, intraday, imbalance prices, along with renewable energy and IDREX market, are tackled via a hybrid stochastic-interval approach. The suggested structure is not only capable of handling both stochastic and interval uncertainties but also can manage the risk associated with both uncertainty characterization methods. To this end, the proposed risk-constrained offering and bidding model turns into a tri-objective optimization problem in which the normal boundary intersection (NBI) procedure is applied for its solution. The numerical results demonstrate that the proposed framework is well capable of simultaneously reaching risk-taker and risk-averse strategies.

Risk-constrained design of autonomous hybrid refueling station for hydrogen and electric vehicles using information gap decision theory

The proposed autonomous hybrid charging station in this paper is energized by a photovoltaic (PV) system, which should provide electric vehicles (EVs), and water electrolyzer (WE) with electricity. The WE operates by using electricity to produce and store hydrogen to feed hydrogen vehicles (HVs). Moreover, a fuel cell (FC) is allocated to the system, which uses the stored hydrogen to regenerate electricity the PV system is beyond reach. A supplementary diesel generator is also installed in the charging station to avoid power shortage as a conservative measurement. The hydrogen and electric demand of the station is accompanied by uncertainties, which should be taken into account in designing the charging station. Therefore, information-gap decision theory (IGDT) is employed to deal with the uncertainties. This approach provides the investor with three different strategies of risk-averse strategy (RAS), risk-neutral strategy (RNS), and risk-seeker strategy (RSS), which can help the investor with making a better decision. The outcome of the simulation proved that in RAS if the investor decides to invest 13.9% more capital, based on the robustness function, the charging station withstands the 9.6% deviation of uncertain parameters’ fraction error. However, should the investor decide to take risks in the construction of the charging station, by paying 13.9% less, the system is 10.7% fragile to the information-gap of uncertainties. Besides, the rated power of the PV system reaches from 1612 kW in RNS to 1731 kW in RAS while it decreased to 1479 kW in RSS.

Risk-controlled economic performance of compressed air energy storage and wind generation in day-ahead, intraday and balancing markets

In this paper, the wind power aggregator is cooperating with a commercial compressed air energy storage (CCAES) to participate in three markets, including day-ahead (DA), intraday (IN), and balancing (BL) markets. A three-stage stochastic programming problem is formulated to model the optimal operation of the proposed system. In the proposed model, the uncertainties of wind power, as well as DA, IN, and BL markets price is modeled by the implementation of scenario generation and reduction methods. Financial risks imposed from the uncertain parameters are investigated in the proposed stochastic optimization framework. For this purpose, the downside risk constraints approach (DRCA) is modeled in the stochastic formulation to manage the financial risks. In the proposed DRCA, a risk-based strategy can be proposed for the aggregation of CCAES and wind as a power aggregator (PA) that has a controlled risk in the risk-averse strategy. According to obtained results, the expected profit of PA without DRCA is € 4176.3, while the proposed strategy by the DRCA leads to a € 4094.0 profit. Therefore the proposed strategy by DRCA has a lower profit (1.97%) while leads to a guaranteed risk-controlled strategy for PA with the reduced risk by 100%.

Optimal Behavior of a Hybrid Power Producer in Day-Ahead and Intraday Markets: A Bi-Objective CVaR-Based Approach

Coordinated operation of various energy sources has drawn the attention of many power producers worldwide. In this paper, a Concentrating Solar Power Plant (CSPP) along with a wind power station, a Compressed Air Energy Storage (CAES) unit, and a Demand Response Provider (DRP) constitute the considered Hybrid Power Producer (HPP). In this regard, this paper deals with the optimal participation of the mentioned HPP in the Day-Ahead (DA), and intraday electricity markets by benefiting from the joint configuration of all accessible resources. To attain risk-averse strategies in the suggested model, Conditional Value-at-Risk (CVaR) based on the ε-constraint technique is employed, while its efficiency is validated compared to the previously applied method to such problems. On the whole, the main contributions of this work lie in: 1) proposing a novel model for optimal behavior of a CSPP-based HPP in DA, and intraday markets using a three-stage decision-making architecture, and 2) developing a bi-objective optimization framework to improve the functioning of the risk-constrained algorithm. Simulation results reveal that taking advantage of the CSPP in the intraday market, and coordinated operation of all resources not only enhance the profitability of the system but also lessen the associated risk compared to the previous models.

Risk-averse multi-objective optimal combined heat and power planning considering voltage security constraints

In this paper, a comprehensive model is proposed for long-term planning of various combined heat and power units in an integrated heat and electricity network. The proposed model takes into account the uncertain electric loads, and market price applying the information gap decision theory. Furthermore, the security of the power grid from the voltage stability viewpoint utilizing the L-index approach is considered. The model is based on the risk-averse multi-objective combined heat and power planning methodology, which maximizes the profit of combined heat and power owners and minimizes the system operator costs over the planning horizon in the presence of the environmental emissions cost. The best compromise solution is achieved via a fuzzy logic-based min-max method. The risk-averse strategy of Information gap decision theory is applied to the obtained solution, which demonstrates the impact of data uncertainty. The proposed mixed-integer non-linear programming model is solved using the general algebraic modeling system package and tested on the IEEE 14-bus standard system. The results indicate that the risk-averse strategy improves the robustness of the network against the uncertainty. Also solving the model using the multi-objective framework gives comprehensive results, and shows that the voltage stability constraints affect the planning decisions.

Risk-constrained optimal scheduling with combining heat and power for concentrating solar power plants

Concentrating Solar Power (CSP), a schedulable renewable energy technology, realizes the conversion of “solar-heat-electric”. The benefits from the thermal energy storage (TES) and its potential for heating need to be explored. In desert areas with low population density and relatively concentrated industrial loads, a CSP plant equipped with back-pressure turbines (BT) as generators can use waste heat from power generation to supply heat, which effectively improves the energy production efficiency of CSP plants. Thus, a combined heat and power scheduling model for CSP plants to participate in heating is established in this paper. In order to cope with the uncertain parameters of the model, the information gap decision theory (IGDT) is introduced to describe them. IGDT can draw on the risk-seeker strategy and risk-averse strategy to deal with the uncertainty, and thus provide a reasonable decision basis for the scheduling operation. In addition, we ensure that the storage cycling of CSP plants is energy neutral, which could allow the plant to optimize its output for multiple consecutive days to meet demands. Finally, the model is verified to ensure that the predefined profit level could be achieved and the corresponding scheduling strategy under different operational risk is obtained.