Downside Risk Constraints Research Articles

Risk-embedded scheduling of a CCHP integrated with electric vehicle parking lot in a residential energy hub considering flexible thermal and electrical loads

Climate change has detrimental impacts on the environment and sustainability, leading to employing alternative energy systems such as combined cooling, heat and power (CCHP). The concept of integrated energy systems (IES) allows the coordination of several components, such as electric vehicles (EV), to serve various demands simultaneously. This paper focuses on coordinating the CCHP and electric vehicle parking lot (EVPL) integrated with photovoltaic (PV) technology as renewable energy (RE). The residential energy hub (REH) is modeled to integrate these components to meet the demands and minimize REH’s operating costs and carbon emissions. EVPL functions as dynamic electrical storage besides serving EVs. Stochastic programming is used to model RE, EV, loads, and electricity price uncertainties. Demand response (DR) is applied for shiftable electrical loads. The thermodynamic model of heating and cooling loads is developed with flexibility as integrated demand response (IDR) based on the building’s desired temperature. The emission cost model with penalty factors enforces REH to use less-pollutant energy sources. Subsequently, a risk-aversion strategy, namely downside risk constraint (DRC), is implemented to diminish the associated risk as the consequence of uncertainties for the decision-maker. Different constraint level is applied to provide various conservative decision-making strategies for the operator. Summer and winter scenarios with and without DR and flexible thermal loads were used to evaluate the model’s accuracy. The scheduling problem is solved in IEEE 33-bus test system. The results reveal that the DR could reduce the operation cost by 5% in summer and 8% in winter. Moreover, zero risk for summer and winter is gained at the cost of 10.4% and 3.1% increment in operating costs.

Two-stage risk assessment of the electricity retailers in the day-ahead energy and primary-secondary reserve markets with flexible demand responses and parking lots

The main entities in the deregulated environment are electricity retailers that can directly connect with the demand consumers and power producers. One of the markets that retailers are willing to participate is the primary and secondary reserve markets. This article aims to deal with the retailer's self-scheduling problem that aims to maximize their performance in both reserve and energy markets while minimizing their financial risks. To participate in these markets, retailers need flexible resources like demand response programs (DRPs) and electric vehicle parking lots, which are considered. Under this situation, electric retailers are subjected to different uncertainties related to the difference in vehicles' behaviors, such as arriving and departure times and the percentage of the initial state of charge, the difference in characteristics of EV's batteries due to the variety of factory brands, and also the markets-based uncertainties. In addition to the mentioned uncertainties, EVs' similarities in battery characteristics and behavior patterns of owners are also considered in the scenario generation procedure. Modeling the worst realizations of uncertainties significant impacts electricity retailers' decision-making on such extreme uncertainties; therefore, the downside risk constraints (DRC) methodology is used to model the financial risks of the introduced high-risk stochastic problem. Due to the high number of uncertain parameters, the generated scenarios are also reduced using a distance-based method. According to the obtained results, by accepting a 14 % lower profit, retailers can receive a strategy with close-to-zero risk levels. Besides, the participation of retailers in the fast reserve market is more suitable than the slow one due to higher prices.

Integration of hydrogen storage system and solar panels in smart buildings

In the residential area, the cost of the electricity is at the maximum level during the peak demand periods. Regarding this, the peer-to-peer (P2P) energy trading strategy is a hopeful and practical solution to deal with this problem. Also, in the P2P home energy management system, the buildings have become more flexible by sharing their powers to meet their electricity demands. In this work, we have considered three buildings, in which two of them are equipped with solar panels, and in the third one, the hydrogen source is established. In the proposed residential zone, the considered buildings are connected in order to perform P2P transactions to reduce the energy billing. The operator of the system is wholly investigated under uncertain conditions based on the downside risk constraint (DRC) method in both risk-averse and risk-neutral models. With the help of a P2P energy trading strategy as well as renewable energy resources, we can provide electricity to customers in remote areas where it is not possible to supply electricity or is not economically beneficial. Based on the obtained results, the function of the proposed system has manifested in an acceptable manner. For instance, in the solar panel function in the second building, the amount of generated solar power is 30 kW in the risk-averse model, whereas, in the risk-neutral model, it is 18 kW. On the other hand, the level of consumed electricity by the electrolyzer system is generally lower in the risk-averse model than risk-neutral model in the second season.

Risk analysis of integrated biomass and concentrated solar system using downside risk constraint procedure

In the electric power industry, solar energy and bioenergy are two energy resources that produce near-zero emissions. In order to generate dispatchable electricity power and make increments in the role of the ever-growing renewable-dominated networks, the concentrated solar energy connected with biomass boiler and thermal storage unit could be used. In this regard, the model of day-ahead and intra-day dispatch is represented for an integrated biomass-concentrated solar (IBCS) system with the aim of maximizing the system profitability by a cooperative association of the elements of the system. Furthermore, to capture the impact of uncertainties on the system function, the investigation of the IBCS system is carried out based on a stochastic optimization procedure called the downside risk constraint (DRC) method, which is a scenario-based procedure. In this method, the function of the elements is explored thoroughly regarding the risk-averse and risk-neutral (under risky condition) strategies (risk is not considered). The simulation results demonstrated that the proposed model of the dispatch can achieve considerable profit for the decision-maker of the system. For instance, the obtained profit in the risk-averse model is 27074.5 $, while in the risk-neutral model, it is 31,347 $. This reveals that the system operator needs to increase %14 of its expenditure in the risk-averse strategy to increase the system's security.

Demand and price fluctuations effect on risk and profit of single and clustered microgrids during COVID-19 pandemic

COVID-19′s widespread distribution is wreaking havoc on people's lives all over the world. This pandemic has also had a significant impact on energy consumption. Its influence can be seen in the power system's operation and the market as well. The power consumers' habits and demand curves have been changed at a breakneck pace. In this work, a one-year mixed-integer programming (MIP) problem has been developed to compare the power consumption between 2019 and 2020 in the United States as an example regarding the COVID-19 pandemic effect in order to better prepare for possible similar future events. 100% renewable single microgrids (SMGs) are studied using wind turbines and photovoltaics. Batteries are also employed since it is inevitable when the system uses renewables. Additionally, it is possible for the SMGs to trade power with the main grid as needed. The effect of the SMGs’ clustering to form the multi-microgrids (MMGs) is also considered. In order to investigate the risk of the system during the COVID-19 and formation of MMG, downside risk constraints are applied to the proposed model. Furthermore, a stylized short-run consumers demand model is proposed, using elasticity and assessed responses regarding the average household consumption for households during on-peak and off-peak periods. The simulation results show that COVID-19 generally reduces the demand, increases the profit of the system, and decreases the economic risk of the power system’s operation. Moreover, SMGs clustering to organize MMG dramatically enhances the profit of the system as well as improves the risk level of the system.

RETRACTED: Risk-based design of hydrogen storage-based charging station for hydrogen and electric vehicles using downside risk constraint approach

• Optimal design of off-grid hydrogen and electric vehicles charging station. • Uncertainties of solar irradiance, electricity and hydrogen loads are considered. • A scenario-based stochastic optimization model is proposed to model uncertainties. • Downside risk constraints method is proposed to manage risk related to uncertainties. • Risk-averse state of the model is presented and compared to the risk-neutral state. This paper proposes an off-grid photovoltaic powered charging station for electric and hydrogen vehicles. The charging station simultaneously feeds hydrogen and electric vehicles during all hours of the day. Therefore, fuel cell and hydrogen storages systems are designated to the station to meet the demand during the inaccessibility of the solar system. Furthermore, a diesel generator is allocated to the system in case the severe impacts of uncertainties included in the energy profile of the solar system and the loads lead to power shortage. Downside risk constraints are integrated into the scenario-based stochastic programming model to handle the risks involved as a novel framework. The advantage of this approach to others is providing a scenario independent risk analysis by offering one specific expected cost for all scenarios. The risk-neutral mode of the project is contrasted with the risk-averse state to depict the efficiency of the framework. The comparisons confirm that a 4.31% increase in annualized cost leads to a remarkable reduction of risk-in-cost by 50%. As a result, the nominal capacity of the diesel generator increased from 78 to 109 kW in the risk-averse state. However, the installed capacity of the photovoltaic system remained constant for all risk levels at 1688 kW.

RETRACTED: Risk modeling of an industrial area with shared storage of several buildings using downside risk constraint method considering peer-to-peer trading

The Peer-to-peer (P2P) trading strategy is one of the valuable methods that could be used in various local electricity markets with different aims, such as peak shaving and energy cost reduction. In this paper, due to the enforcement of financial risks, a risk evaluation procedure called the downside risk constraint (DRC) method is applied to analyze the effects of the P2P method and shared storage trading in an industrial area in both risk-neutral risk-averse models. The industrial zone includes various buildings that are connected to each other and include different DERs. The investigation is performed in three separate cases: base case, P2P case, and P2P+shared storage case. According to the obtained results, for a high-level risk, we need to spend more currency to keep the system's security at a high level. For example, the amount of achieved cost in the risk-averse model is 816,000$ in the third case. Besides, the potential savings are attained in electricity costs; for instance, in the risk-neutral model, 14.8% of electricity cost is reduced in the second and 21.5% in the third cases. The reduction of electricity price is because the amount of purchasing power from the grid is declined with the help of DERs.

Optimal offering of wind-photovoltaic-thermal generation company in energy and reserve markets in the presence of environmental and risk analysis

The integration of thermal units with renewable energy sources is increasing to reduce greenhouse gas emissions and fossil fuels consumption. Also, renewable energy sources such as wind farms (WF) and photovoltaic farms (PVF) can be used by Generation Company (GenCo) to maximize their expected profit. Providing the offering curves for each hour under the uncertainties as electricity and reserve prices, an imbalance price ratio for energy deviations and output powers of PVF and WF is the other important issue for the GenCo’s operator. Therefore, an ε-constraint approach is used in this paper to handle the environmental and economic constraints simultaneously in the proposed Wind-Photovoltaic-Thermal Generation Company (WPT-GenCo). On the other hand, a Downside Risk Constraints Technique (DRCT) is used to handle the risk associated with various considered uncertain parameters in the proposed WPT-GenCo. Besides, obtaining robust offering strategies considering a specific level of greenhouse gas emissions is the other target of this paper. Finally, the TOPSIS method is used to select the trade-off solution through the 3-dimension obtained results of expected profit, greenhouse gas emissions, and the risk associated with various uncertainties. Simulation results show that the proposed WPT-GenCo model is scheduled robustly, and the risk level is reduced dramatically, considering a specific level of greenhouse gas emissions.

Risk-Based Electrical-Thermal Scheduling of a Large-Scale Virtual Power Plant Using Downside Risk Constraints for Participating in Energy and Reserve Markets

Risk-Based Electrical-Thermal Scheduling of a Large-Scale Virtual Power Plant Using Downside Risk Constraints for Participating in Energy and Reserve Markets

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%.

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%.

Risk management of a renewable-based compressed air energy storage system using downside risk constraints approach

Abstract The financial risks imposed from the uncertain parameters is a considerable issue in the optimization problem of renewable-based energy systems. Due to the various risks in renewable-based energy systems, a practical and simple risk measurement approach can be efficient in the risk-based strategy selection process. In this paper, the downside risk constraints (DRC) approach as a novel risk measurement approach is proposed to manage the imposed risks from the uncertain parameters over the stochastic problems. Therefore, various uncertainties, including solar irradiation, temperature, wind speed, electricity demand, and electricity market price uncertainties, are modeled using the DRC approach along with the stochastic programming. In addition, the compressed air energy storage (CAES) and demand response program (DRP) is implemented to manage the imposed risks. By using the proposed risk measurement method, the system operator can obtain a risk-strategy that is independent of scenarios. According to the obtained results, the expected cost of the stochastic problem is $ 6145.62, which by using the DRC approach, the system operator by paying the 3.4% ($ 6353.5) more cost can guarantee itself against the financial risks. The advantage of the proposed DRC approach is the conversion of the stochastic problem to a deterministic scenario-independent problem.

RETRACTED: Risk-constrained optimal operation of fuel cell/photovoltaic/battery/grid hybrid energy system using downside risk constraints method

Residential consumers have electrical and thermal loads. Therefore they can be utilized hybrid thermal and electrical energy systems to procure their required energy. In the proposed system, in order to supply residential loads, a hybrid energy system (HES) is proposed which consists of photovoltaic/solid oxide fuel cell/thermal and electrical storages/boiler. Also, the uncertain parameters such as thermal and electrical loads, electricity market price, and solar irradiation are considered in the stochastic formulation. Uncertain parameters can be led to financial risks in the system operation. In order to measure imposed risks, in this paper, a novel risk management method called downside risk constraints method is used to model the financial risks imposed from the uncertain parameters. According to obtained results, the operator of the hybrid energy system by utilization of the downside risk constraints method has obtained a strategy that is scenario independent. In other words, the downside risk constraints method by minimizing the imposed risks introduced a zero-risk strategy which operation cost would not increase by changing the scenario. Results are shown that system operators by paying 1.3% more expected cost ($ 40.22 instead of $ 39.69), can make its operation independent of the scenario. Also, risk-based operational strategies of the proposed hybrid energy system are reported in the results as graphical results. The proposed risk-measurement operation problem of the designed hybrid energy system is formulated as a mixed-integer linear programming (MILP) model and modeled by GAMS software using CPLEX solver.

Risk and profit-based bidding and offering strategies for pumped hydro storage in the energy market

Pumped hydro storages (PHS) are the most common storage in the power system, which covers 99% of the total installed capacity of energy storage facilities in the world. Therefore, optimal offering and bidding strategies of PHS are essential in the energy market. Besides, various uncertainties, especially market price uncertainty is more challenging for PHS operator due to the imposed risks. Therefore, in order to optimally market-participation of these storages, in this paper a novel risk management approach called downside risk constraints (DRC) is applied to get the optimal offering and bidding strategies of the PHS. The DRC method is used along with the stochastic programming to model the risk associated with uncertainty parameter, including electricity market price uncertainty. Hence, ten scenarios are produced using the normal distribution function to model the uncertain parameter. Illustrated examples are determined how to deal with imposed risk from uncertainty by managing financial profit. Finally, according to the obtained results, using the proposed downside risk constraints, PHS operators can experience zero-risk conditions by decreasing their profit by 33% while risk-in-profit is reduced by 100% in versus risk-neutral strategy.

RETRACTED: Risk-involved stochastic scheduling of plug-in electric vehicles aggregator in day-ahead and reserve markets using downside risk constraints method

Abstract A single plug-in electric vehicle (PEV) cannot participate in reserve and day-ahead markets as they cannot meet the energy requirements of independent system operators (ISO). However, they can be gathered by a PEV aggregator and play a role in so called markets. On the other hand the PEV aggregators are to deal with the uncertainties that go along with these markets and can highly affect their profit. In order to cover these uncertainties scenario-based stochastic approach can be taken into to account to optimally schedule the PEV aggregators so that the maximum profit is obtained. The main contribution of this paper is to involve risk related uncertainties through the downside risk constraints (DRC) which results in risk-constrained stochastic optimization model. The main advantage of this method is that it can provide the owner of PEV aggregator with decisions that are made by considering various quantities for risk. CPLEX solver of GAMS software is employed to solve the problem which is formulated as mixed-integer linear programming (MILP) model. To investigate the accomplishment of DRC, risk-averse state of model is compared to risk-neutral which in former one the profit is reduced meanwhile that risk-in-profit (RIP) is declined.

RETRACTED: Risk-constrained stochastic power procurement of storage-based large electricity consumer

Abstract Large electricity consumers can be either a large industrial consumer or a coalition of small electricity consumers. Large consumers (LCs) confront with various uncertainties due to the use of various power resources in the power procurement process, such as renewable resources, self-generation units, forward contracts, and pool market. These uncertainties can be lead to many financial risks for LCs. In this paper, the stochastic power procurement problem of large consumers is solved, and the new risk-measurement method is used to analyze the large consumer risks in power procurement process. The mentioned risk-measurement method is called downside risk constraints (DRC) method, which is used to model the financial risk imposed from uncertain parameters along with the stochastic problems. According to obtained results, it can be concluded that DRC method is a non-equilibrium method, which is applied clearly as a constraint to the optimization problem. In addition by using the DRC, LC can experience lower-risk strategy in the power procurement problem. Also, using DRC can make the total cost of large consumer independent of scenarios, which led to the lower-risk experiencing by the large consumer. Finally, results are expressed that lower-risk cost in DRC is less than the cost of the worst scenario in stochastic programming.

Risk and Uncertainty Analysis of Cooling Demand in Multi-Chiller System Using Downside Risk Constraints Method

The optimal performance of a multi-chiller system (MCS) is the crucial factor in managing the expected power consumption (EPC). Also, the uncertainty of cooling demand in the industrial or residential sector plays a crucial role, which should be modeled and managed. So, the stochastic risk-constrained performance of the optimal chiller loading is studied in an uncertain environment in this paper. Scenario-based stochastic programming is applied to the provided case study to model the cooling demand uncertainty (CDU), and the downside risk constraints (DRC) are implemented to model the associated risks. The risk-averse performance of the MCS is compared with the risk-neutral one to show the positive effects of the DRC. The proposed model is implemented under the DICOPT solver in GAMS software. The comparison results show that the expected power consumption of MCS is increased slowly, while the expected risk-in-power consumption (ERIPC) is decreased promptly.

Risk-Based Traded Demand Response Between Consumers’ Aggregator and Retailer Using Downside Risk Constraints Technique

Electricity retailer is the most critical player in the restructured electricity market. Retailer's electricity procurement problem is a big challenge for them. Electricity retailer can purchase their energy from various options such as pool market, forward contracts, and etc. A relatively new way with a lesser investigation is the demand response exchange market. In this paper, the risk-constrained stochastic power procurement problem of electricity retailers is formulated by modeling the uncertainty of pool-market price and consumers' electricity demand. The Downside risk constraints (DRC) risk-evaluation method is used to obtain risk-based power procurement scheduling of electricity retailers. By using the proposed method, conservative electricity retailer can experience a scenario-independent strategy over the stochastic optimization. In other words, the proposed risk strategy is such that impose more cost for electricity retailer while having an equal cost in all scenarios. Based on the obtained results, the operation cost of electricity retailers in all scenarios is closed to about $4827570, which is a relatively zero-risk strategy due to equality overall scenarios. Besides, results are compared in two cases to demonstrate the advantages of the proposed risk-evaluation method. Also, the Pareto front between risk-in-cost and expected cost can introduce an optimal risk-strategy for electricity retailers in the presence of uncertainties. Finally, the risk-averse strategy of electricity retailer is proposed to obtain the retailer's optimal conservative schedule during power procurement in the presence of uncertainties.

Offering Decision of Risk-Based Wind-Photovoltaic-Thermal GenCo Using Downside Risk Constraints Approach

Risk analysis and scheduling of a Generation Company (GenCo) under uncertain environments are challenging issues. So, stochastic optimization and downside risk constraints approaches are used in this paper to model and manage the risk associated with various uncertainties. The presented GenCo model comprised five thermal units, photovoltaic systems, and wind farms. It is assumed that all thermal units, photovoltaic systems, and wind farms can participate in the energy market. In contrast, only thermal units can participate in the reserve market. The uncertainty of electricity and reserve market prices and output power of photovoltaic systems and wind farms are modeled via a stochastic optimization approach. Afterward, the downside risk constraints method is used to manage the risks associated with various uncertainties. By analyzing the obtained results, it can be seen that the level of the average risk can plunge to 0 by gaining 4.68% less average profit. So, the GenCo can be immune against considered uncertainties with gaining a little bit less profit. Furthermore, the offering strategy is studied in two risk-neutral and risk-averse strategies. Finally, the CPLEX solver of GAMS software is used to optimize the studied linear-based model of GenCo.

Environmental and Economic Operation of Wind-PV-CCHP-Based Energy System Considering Risk Analysis Via Downside Risk Constraints Technique

Combined Cooling, Heating, and Electrical (CCHP)-based microgrids meet various energy demands at the same time. Risk analysis of such CCHP-based microgrids under environmental and economic constraints is a challenging issue. Furthermore, high penetration of various renewable energy sources in such CCHP-based microgrids clouds the issue. Thus, Downside risk constraints technique (DRCT) is employed in this paper to manage and minimize the risk associated with various uncertainties in the studied sample CCHP-based microgrid under environmental and economic constraints. The uncertainties of solar radiation, wind speed, market tariff and cooling, heating, and electrical demands are considered, and the stochastic optimization approach is used to model the mentioned uncertainties. The effect of the Real-Time Pricing rate of the Demand Response Program (RTP-DRP) has been studied in this paper. By analyzing the obtained results, it can be found that with considering DRCT, the risk-in-cost (RC) level can plunge to 0 respectively by paying 2.61% and 1.68% without and with RTP-DRP.