Risk-neutral Strategy Research Articles

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

AbstractIn distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO.

Stochastic risk-embedded energy management of a hybrid green residential complex based on downside risk constraints considering home crypto miners, adaptive parking lots and responsive loads: A real case study

Hybrid energy systems can improve the economical and technical aspects of different infrastructures due to their high efficiency and flexibility. In this study, the energy management of a hybrid green residential complex composed of renewable resources, electric boiler/chiller, and electrical storage is optimized considering adaptive parking lots, home crypto miners, and responsive loads. Since the reliable operation of such structures requires accurate predictions, various parameters including local loads, solar radiation, wind speed, arrival/departure time of vehicles, initial charge of batteries, and price of markets are modeled using scenario generation procedures in the first step. Afterward, the downside risk method is applied to analyze the risk of decisions under the desired robustness level. The proposed planning is implemented on a real network and the simulations are compared with the conditional value-at-risk approach for validation. The results show that in a specified robustness level, the risk-averse attitude increases the cost by about 4.47%, whereas the obtained crypto currency by crypto miners is about 34.67% higher than the risk-neutral strategy due to the availability of local sources. Furthermore, the parking lots can act as bulk storage units and 10% of load flexibility decrease the cost by about 3.53%.

Resilience and risk-based restoration strategies for critical infrastructure under uncertain disaster scenarios

Improving the resilience of critical infrastructure (CI) systems to natural disasters is essential for society's long-term development. Considering the uncertainty in disaster scenarios, this study first proposes a two-stage risk-neutral stochastic model to develop restoration resource deployment and allocation strategies with the objective of maximizing the expected value of resilience (EVR) of a CI system. Secondly, a risk metric is applied to assess the risk on the value of resilience imposed by the uncertainty. And a multi-objective optimization model for risk management is proposed, in which minimizing the value of the risk metric is formulated as a secondary objective to the EVR. The trade-offs between the EVR and the risk objective are assessed via the ε-constraint method. Finally, the utility of proposed models is illustrated using the electric power system of the Greater Toronto Area (GTA). The results show that: (i) there is a significant advantage in using risk-neutral strategies to improve system resilience over strategies without considering the characteristics of disaster scenarios; (ii) the value of the risk metric can be significantly reduced at the expense of decreasing EVR; and (iii) if the use cost of the resources is considered, there are optimal amounts of available resources for risk-neutral and risk-averse CI system operators. The models can assist CI system operators in selecting effective emergency response strategies for disasters with uncertain scenarios.

Who contributes to disaster preparedness? Predicting decision making in social dilemmas pertaining to community resilience.

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web-based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in-game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto-efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto-efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk-neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decisiontheory.

Multi-objective risk-constrained optimal performance of hydrogen-based multi energy systems for future sustainable societies

Hydrogen is one of the leading energy sources under special consideration due to its sustainability and technological development. The main concept of integrated energy system is applied in this article. The goal is to maintain a coordination between hydrogen cycle where hydrogen is produced and converted with renewable energy (RE). Three energy storage for charging/discharging hydrogen, hot and cold water are employed enhance the energy efficiency. The demand-side energy system could rely on this combination that enables the increase in wind and solar energy utilization and uses it to generate hydrogen in future smart cities. This paper uses a scenario-based stochastic optimization to model the associated uncertainties. The epsilon-constraint and max-min fuzzy methods are used as multi-objective approach to satisfy environmental and economic goals, simultaneously. To analyze the associated risk imposed by multiple uncertain parameters, the downside risk constraint (DRC) is implemented to render several risk-averse strategies to the decision-maker based on various constraint level. The results show that the final solution in risk neutral strategy satisfies both emission and operation costs. The ultimate associated risk in conservative strategy will tend to zero at the 4.2% operation cost increment and 3% emission reduction.

A robust multi-objective joint scheduling of integrated electricity and gas grids considering high penetration of wind and solar units and flexible loads towards achieving a sustainable operation

The cooperative planning of interconnected networks to achieve a sustainable operation and satisfy the welfare of all agents has become a critical issue. This paper optimizes the joint scheduling of integrated electricity and gas grids composed of wind turbines, photovoltaics, storage systems, power to gas and gas to power units. In this regard, a risk-constrained multi-objective programming is suggested to simultaneously model the operation costs of both networks and a robust augmented epsilon constraint method is applied to solve the problem and obtain the Pareto set. In order to simulate realistic conditions, uncertainties of wind and solar energies, electrical and gas loads and energy and gas prices are also considered. As well, the impact of flexible electrical and gas loads is analyzed using a time-of-use demand response program. According to the simulations, the presented approach is capable to create a good equilibrium between both networks. The results validate that the risk-averse strategy significantly increases the costs of electrical and gas grids by 21.41% and 10.36% compared to the risk-neutral strategy, respectively. In return, the robustness is improved against fluctuations caused by uncertain parameters. Moreover, responsive loads enhance decision flexibility and decrease the mentioned costs by 2.26% and 7.83%, respectively.

Risk-aware energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources high penetration, crypto-currency miners and responsive loads

The penetration of renewable resources and electric vehicles has increased in recent years due to various benefits such as reducing environmental pollution. This paper optimizes the energy management of a microgrid integrated with battery charging and swapping stations in the presence of renewable resources and crypto-currency miners as an emerging critical load with high energy consumption. In such structures, the fluctuation of renewable energies decreases reliability and increases energy market trading. Hence, the conditional value-at-risk index is utilized to analyze the risk of uncertainties. Furthermore, the influence of responsive local loads and incoming vehicles into the stations is investigated using demand response programs. In order to implement the presented programming, a real distribution network in Kermanshah, Iran, is selected as the case study. The results show that the risk-averse strategy with α = 0.85 and β = 0.8, reduces the expected revenue by about 636.355 $ compared to the risk-neutral strategy. In addition, the swapping station can be used as a bulk storage, where it stores about 43.56 MWh in low-price times and injects about 20.04 MWh in high-price times to support the local system. The demand side management also increases the revenue even under the worst-case conditions by about 79.623 $.

The CVaR-based risk assessment of the electric vehicle's intelligent parking lots with energy storage devices including equalizers and fuel cell

The interest in utilizations of electric vehicles (EVs) causes to increase the penetration of intelligent parking lots (IPL). Also, vehicle-to-grid (V2G) technology next to grid-to-vehicle (G2V) technology improves the profitability of the IPLs. In addition to vehicle charge and discharge management, the IPLs can gain more profit by installing and accessing loads and resources. In this paper, the IPLs next to the hydrogen storage system (HSS) containing electrolysis, fuel cell and hydrogen storage tanks are considered to serve the loads in the existence of the upstream grid that is modeled through a scenario approach based on stochastic optimization. The uncertainties related to the electrical load, market price, vehicle's arrival and departure time, initial State-of-charge (SOC), and desired SOC of the car are considered in the proposed model. Besides, the financial risks related to the proposed hydrogen storage-based intelligent parking lot's uncertain parameters are modeled by the conditional value-at-risk (CVaR) method to get the risk-averse and risk-neutral strategies during the system operation horizon. The obtained results demonstrate that the uncertainties have a significant impact on smart parking operators' profitability, so considering uncertainty is a critical issue for parking lots. Risk results also represent that the variation of financial risk in the higher deviation of uncertain parameters is more than risk variation in lower deviations.

RETRACTED: Pumped hydro energy storage arbitrage in the day-ahead market in smart grid using stochastic p-robust optimization method

• Individual participation of pumped hydro energy storage (PHES) in day-ahead market. • Use of stochastic optimization to model the day-ahead market price uncertainty. • Stochastic p-robust optimization (SPRO) is proposed to measure the financial risks. • Risk-guaranteed strategy is obtained in comparison with risk-neutral strategy. • Robust bidding and offering strategies for PHES are obtained with SPRO method. Individual operation of pumped hydro energy storage (PHES) as large-scale energy storage needs the bidding and offering curves to participate, as a prosumer, in the day-ahead electricity market. Therefore, in this paper, by taking advantage of stochastic and robust optimization, a new stochastic p-robust optimization (SPRO) method is proposed to deal with the financial risks of the day-ahead price uncertainty. The proposed method compares the profit of PHES with the relative regret to get a risk-controlled strategy. According to obtained results, reducing the robustness parameter (p) from the p=+∞ to p=0.21, the profit, and the maximum relative regret are reduced by $ 2611 and 15.02 %, respectively. In other words, with a 19.11 % decline in profit, the maximum amount of relative regret decreased by 41.30 %. Therefore, it can be shown that by a few reductions in profit, the maximum amount of relative regret decreases significantly. At the same time, the robustness of PHES is guaranteed against the day-ahead market price uncertainty. Finally, the risk-based bidding and offering curves are derived using the results of the proposed SPRO method.

Risk-averse operation of energy-water nexus using information gap decision theory

Nowadays, nexus systems spread in modern societies because of their ability in incorporation of the dependencies of the interconnected resources water and energy. Other parameters such as cost-effectiveness, environmental issues can be considered as the advantages of the mentioned system. Besides the mentioned positive features, the most significant barrier, the uncertainty of the demand energy, still needs to be taken into account. So, information gap decision theory (IGDT) is applied to the proposed nexus system to analyze the water and power demand uncertainties levels. GAMS software is used to solve the proposed risk-based model. In the proposed IGDT method, risk-averse and risk-neutral strategies are applied to the proposed nexus system, and the obtained results are analyzed with each other. Obtained results indicate the robustness of studied system under uncertainties of water and power demands. For instance, α=5.7%, total robustness cost of the studied nexus system is increased by approximately $250.

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.

Systematic risk in pairs trading and dynamic parameterization

In statistical arbitrage, pairs trading is usually considered a risk-neutral strategy. However, the methodologies in existing literature on choosing thresholds and calibrating cointegration coefficients could be arbitrary and insensitive to market changes. This research discovers that static parameterization in pairs trading could result in undesirable systematic risk and potential losses. We apply Kalman Filter to intertemporally estimate cointegration coefficients and the absolute standardized residual (ASR) threshold, and relate the ADF-threshold with stochastic discount factors. Backtests confirm our superiority in attaining better risk-to-reward ratios and lower systematic risk.

Who Profits From Trading Options?

We use account-level transaction data to examine trading styles and profitability in a leading derivatives market. We find that retail investors in particular favor a consistent trading strategy: approximately 70% of retail investors predominantly hold simple one-sided positions in only one class of options, while institutional investors are more likely to use multiple strategies with a range of complexity. Accordingly, we use trading style complexity as an ex ante measure of trading skills to demonstrate its significant effect on investment performance. We find that retail investors using simple strategies are losing to the rest of the market. For both retail and institutional investors, volatility trading earns the highest return, and risk neutral strategies deliver the highest Sharpe ratio. We conclude that these style effects are persistent and cannot be explained by systematic risk exposure or known behavioral biases.

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 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.

Risk-Based Performance of Combined Heat and Power Based Microgrid Using Information Gap Decision Theory

In this paper, to get optimal scheduling of microgrids (MGs) in short-term, info-gap decision-theory (IGDT) is applied to assess load uncertainty. In order to provide a comprehensive load uncertainty study, best and worst possible conditions are evaluated using IGDT's functions of opportunity and robustness, respectively, in which risk-taker and risk-averse strategies are obtained while deterministic case study is also carried-out under the risk-neutral strategy. Real-time pricing (RTP) and time-of-use pricing (TOU) of demand response program (DRP) are applied to peak-load management. For each strategy, three cases as without DRP, TOU-DRP, and RTP-DRP are investigated. According to the results in the risk-neutral strategy, by applying TOU-DRP and RTP-DRP, operating cost is reduced about 2.5% and 6.6%, respectively. In the risk-averse strategy, by considering MG operating cost equal to $6,000, the MG will robust against load uncertainty up to 18.37%, 21.52%, and 24.82% for without DRP, TOU-DRP, and RTP-DRP cases, respectively. In the risk-taker strategy, MG operating cost for 28% of load reduction, operating costs will be equal to $3,698.12, $3,605.21, and $3,338.87 for without DRP, TOU-DRP, and RTP-DRP, respectively, in comparison with the risk-neutral strategy.

Risk evaluation and retail electricity pricing using downside risk constraints method

Electricity in the retail market has a different value for different types of consumers. Therefore, different retail prices are usually determined for various consumers in the retail market. However, imposed risks from uncertain parameters are a big challenge in the real-time retail market pricing process. This paper proposed a real-time pricing (RTP) framework for various users including residential, commercial, and industrial consumers by the electricity retailer. In addition, uncertainties of various input parameters such as output power of renewable energy resources, electricity demand, and pool market price are modeled using scenario-based stochastic approach while downside risk constraints method is proposed to model risk associated with uncertainties. By implementing this method, electricity retailer will be able to select various risk-based strategies. Furthermore, numerical results illustrate the various risks versus various profits by the occurring of each scenario which helps the retailer for decisions-making in different scenarios. According to obtained results, retailer by choosing of zero risk strategy can reduce its risk by 100% while expected profit is reduced by 2.07%. In addition, offered RTP by the retailer is higher for industrial, commercial, and residential customers, respectively. Finally, risk-averse and risk-neutral strategies of electricity retailer are determined in the power procurement problem.

RETRACTED: Risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles using downside risk constraints method

Today, the utilizations of hydrogen storage systems (HSS), renewable generation units (PV and wind generation) and distributed energy units are increased in the intelligent parking lots (IPL) in order to charge the electric vehicles (EVs) with clean energy sources. In this work, the uncertainties of upstream grid price, the demand of IPL, wind speed, solar irradiation and temperature are modeled via scenario approach based on stochastic programing. Furthermore, the downside risk constraints method (DRCM) is applied to consider risk related to uncertainties to get risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles. The proposed risk-based formulation is modeled using mixed-integer linear programming (MIP) which is implemented under GAMS software and solved via CPLEX solver. Two cases namely risk-averse and risk-neutral strategies are studied and compared to show the effects of DRCM implementation. The obtained results demonstrate the expected performance cost (EPC) of IPL is slowly raised while risk-in-cost (RIC) is significantly reduced due to model of risk related to uncertainties.

RETRACTED: Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique

Abstract Large electricity consumers (LEC) can purchase energy from various energy resources such as bilateral contracts, pool market, micro-turbines, battery storage systems, wind turbines, photovoltaic panels (PV). The uncertainty of market price leads to uncertainty in the total cost to the LEC. Therefore, in this article, the robust optimization (RO) technique is provided to investigate the uncertainty of the pool market price in the presented problem. Also, demand response program (DRP) is provided to decrease the purchased cost to the LEC as much as possible. According to the obtained results, without considering DRP, purchased cost is approximately $40,253.252 and $42,586.984, respectively in the risk-neutral strategy (ideal condition) and robust strategy (worst condition). Furthermore, the purchased cost is reduced nearly $36,945.362 and $39,789.267 in the risk-neutral and robust strategies with considering DRP. So, it can be concluded that the purchased cost to the LEC with considering DRP is reduced 8.2% and 6.5% in risk-neutral and robust strategies, respectively.

RETRACTED: Risk-averse stochastic operation of a power system integrated with hydrogen storage system and wind generation in the presence of demand response program

Wind generation (WG) units as renewable energy sources (RESs) are increasing in the world due to environmental functions and lack of conventional energy sources. Also, hydrogen storage system (HSS) as an energy storage system (ESS) is used to cope with variable nature of RESs in which the concepts of power to hydrogen (P2H) and hydrogen to power (H2P) are defined. In this work, a risk-averse stochastic operation of HSS and WG is modeled using a scenario-based stochastic approach by considering price-responsive demand response (DR) program. All uncertainties are modeled via a scenario-based stochastic approach while the risk related uncertainties are modeled via the downside risk constraints (DRC) to capture the risk-averse operation of the HSS and WG. In order to investigate the impact of DRC implementation, a risk-averse strategy is compared versus risk-neutral strategy. Compared results show that the risk-in-cost (RIC) is reduced while the expected operation cost (EOC) is raised to deal with the risk of the uncertainty.