Downside Risk Constraints Research Articles

Economic and risk based optimal energy management of a multi-carrier energy system with water electrolyzing and steam methane reform technologies for hydrogen production

This paper proposes stochastic programming to optimal economic scheduling problems of a sustainable integrated energy system with renewable resources including power, natural gas, and hydrogen carriers. Hydrogen is considered a byproduct of electrical power and natural gas carriers using water electrolysis and steam methane reform process technologies. The main objective is to minimize the operation and emission costs while addressing uncertainties of electric/heating/cooling demands, electricity price, and renewable generation using scenario-based stochastic programming. To achieve a risk-hedging strategy, downside risk constraints are involved to minimize the risky scenarios portfolio. The whole problem is modeled as mixed-integer linear programming in GAMS optimization software. The analysis revealed that the expected operation cost without DRC is $ 577.52 and the expected risk-in-cost value for risky scenarios is $ 470.7. However, a conservative decision for reaching zero risk is costly and increases the expected cost up to $ 2901.1, which is not economically viable. A modest decision-making strategy compromises the cost and risk values where for decreasing the risk-in-cost to $ 288.7 (36.8%), the expected cost increased by to $1010.39 (75.95%). Moreover, the risk-averse decision-maker lowers the power sold to the grid, increases the use of water electrolyzing instead of the SMR process to reduce the environmental charges, and tries to increase flexibility with the help of hydrogen and battery storage.

Sustaining power distribution network submerged in plug-in hybrid CNG-electric vehicles for future green transportation: A risk-constrained analysis

The urgent need to address environmental issues, particularly the reduction of greenhouse gas emissions, has become a global priority. In this pursuit, various technologies are being explored to facilitate the promotion of sustainability. This study focuses on a virtual energy hub (VEH) integrated with Plug-in hybrid gas-electric vehicles (PHGEVs), various energy sources, and storage devices. However, the presence of fossil-based energy sources like combined heat and power (CHP), diesel generation (DG), boilers, and gas-based operation modes of PHGEVs, resulted in the release of carbon emissions. In this concern, the power-to-gas (P2G) technology, with its immense potential, is being considered to absorb and recycle captured CO2 emissions into methane gas, thereby enhancing economic and sustainability perspectives. Furthermore, uncertain parameters including the charge and discharge of PHGEVs, the price of the day-ahead market for heat and power, energy demands, and wind speed are considered in the VEH, the management of which is achieved through the downside risk constraint (DRC) method. The proposed model is implemented and tested on an IEEE 33-bus test system. Results show that P2G reduces the CO2 level by a mean of 3.7 %. Also, for the minimum of 3.2 % profit reduction, the risk level has fallen by nearly 100 %.

Photovoltaic-penetrated power distribution networks’ resiliency-oriented day-ahead scheduling equipped with power-to-hydrogen systems: A risk-driven decision framework

Hydrogen energy and the pioneering power-to-hydrogen (P-2-H) systems have garnered significant interest due to their potential to revolutionize power systems, offering exceptional long-term energy storage capabilities and augmenting the resilience and flexibility of the grid. This paper presents a novel approach for optimizing the day-ahead proactive scheduling of power distribution networks with high solar penetration. The approach utilizes power-to-hydrogen (P-2-H) technology to enhance resiliency and mitigate risks. Uncertainties in solar irradiation, wind speed, electricity demands, and day-ahead market prices are modeled using a scenario-based probabilistic procedure. The optimization problem is formulated as a mixed-integer linear program that minimizes the network's expected normal and resiliency costs while incorporating downside risk constraints. The approach is applied to an IEEE 33-bus power distribution network, demonstrating the effectiveness of P-2-H facilities and risk assessment in improving resiliency during normal and power outage scenarios. Implementing P-2-H facilities in circumstances with and without risk results in a reduction of total operation costs by 40.89% and 40.32%, respectively, according to the simulation findings. Furthermore, the numerical analysis results show that the network's overall operation cost increase for achieving zero risk is only 3.93%, which reduces to 2.32% with the use of P-2-H facilities.

Optimal operation of integrated electricity and gas networks with risk analysis using downside risk constraints method

Recently, the number of power plants producing electricity by natural gas-fired generations is increased. The increased number signifies an inseparable relevance between gas-fired power plants and gas distribution networks. There are different uncertain parameters such as power cut and fluctuation-based market price that are unexpected. They impose financial threats to the performance of the gas and power system. In this study, the downside risk constraint (DRC) method is used by considering market price as an uncertainty. The proposed approach implemented for the non-linear gas flow to assess the coordination between gas and electricity systems properly, in which the financial risks of the cooperation with neighbor zones are regarded. The objective is to minimize the operation cost of the first zone considering the power trading with other zones, in which the proposed approach is applied over a 20-node natural gas (NG) network and IEEE RTS 24-bus. According to the obtained results, to achieve zero risk level in the risk-averse mode, the average cost is increased by 10.3 %. Therefore, it is clear that the possibility of risk has significantly reduced with an increment of operating cost.

Towards a low-carbon and low-risk community: Risk-constrained optimization of energy pricing in electricity and gas networks with flexible carbon-responsive loads

This paper proposes carbon-based nodal pricing, which utilizes carbon-tracing analysis in its structure. The carbon-tracing algorithms enable the independent system operator (ISO) to better manage and establish a fair pricing mechanism, considering the actual contribution of each plant to the network's total daily emissions. The ISO, by utilizing carbon-tracing analysis, can easily compute nodal carbon intensity and branchial carbon flow, which are used in nodal pricing analysis. Furthermore, in this paper, for motivating flexible consumers, especially those who have higher impacts on carbon emissions, a carbon-based incentive framework called the flexible carbon-responsive (FCR) program is established, which helps the consumers control their carbon output and enables the ISO to decrease the network's total carbon emissions. In the proposed structure, the worst-case scenarios, which arise from uncertain parameters, can easily affect the simulation results. As a result, in order to avoid such issues, analyzing risks associated with uncertainty is the next focus of this research, and the downside risk constraints (DRC) approach is used to accomplish so. The DRC technique helps the ISO achieve an appropriate balance between the cost associated with risk and the anticipated cost, with the simultaneous objective of minimizing overall risk. Last, a modified IEEE 24-bus transmission system and the IEEE 20-bus gas network are utilized to evaluate the suggested carbon-based pricing mechanism. The simulation results show that applying the FCR program and carbon-based pricing causes the total operation costs to decrease by 100%, resulting in an increase of only 0.71% under the proposed DRC method. It could be noted that the total operation cost increment in the with-carbon tax case is 0.18% lower compared to the no-carbon tax policy.

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Risk-based operation of plug-in electric vehicles in a microgrid using downside risk constraints method

Optimal risk-driven operation of renewable-penetrated distribution network during natural-disasters: A resiliency-oriented analysis

The increasing penetration of renewable energy sources in distribution networks has brought new challenges in ensuring reliable and resilient operation during natural disasters. The proposed approach considers the uncertainties associated with renewable energy sources, natural disasters, and demand using scenario-based stochastic programming to minimize the expected operational cost of the distribution network. Due to the huge uncertainties related to renewable resources, an efficient risk analysis is applied to obtain a sense of the worst realizations of uncertainties using the downside risk constraints method. Besides, two types of demand response schemes (DRSs) are considered to prevent widespread blackouts in the distribution network. This study provides insights into the integration of renewable energy sources in distribution networks and highlights the importance of considering resiliency optimization of this networks. The effectiveness of the proposed approach is demonstrated through simulations on a 33-bus test distribution system, and the results show successful ride-through of the islanding hours with 100% risk reduction by only 2.91% increase in operation cost over the considered scenarios, while risks may still be present in other set of scenarios. Overall, this research contributes to the investigate the uncertainty-driven operational risks of renewable energy integration of resilient distribution network.

Possibilistic–Probabilistic Risk-Based Smart Energy Hub scheduling considering cyber security in advanced metering infrastructures

Future energy systems will be more efficient because of the energy transition of which, decarbonization, decentralization, and digitalization are important aspects. In addition, cyber security becomes more important in the digitalized energy system as cyber-attacks increase. Since the Smart Energy Hub (SEH) is well known as an efficient type of smart digitalized energy system, cyber security is of great importance in SEH operation. In this paper, a novel possibilistic–probabilistic​ SEH model is proposed to evaluate the effect of cyber-attacks on the demand side of the energy system. The presented model is called Possibilistic–Probabilistic Risk-based Smart Energy Hub scheduling model considering Cyber Security in the Advanced Metering Infrastructure (PPRSEHAMICS). It contains different uncertainties regarding renewable generations, demand response programs, gas fuel, wind turbines, and energy prices. The uncertainty of demand response programs is represented using the Z-number as a possibilistic–probabilistic method. Other uncertainties are represented using a scenario-based hybrid approach and Monte Carlo Simulation. Also, the downside risk constraint (DRC) approach is used to model the behavior of the SEH operator in the presence of uncertainties, so the operation and risk costs are simultaneously optimized. Furthermore, a novel Monte Carlo-based FDI detection/correction method is proposed in order to determine and mitigate the effects of cyber-attacks. The proposed model is formulated as a Mixed-Integer Linear Programming (MILP) problem and the GAMS environment is used to solve it. In this paper, different scenarios are considered to evaluate the effects of demand response (DR) programs, SEH operator behavior, false data injection, and correction on the operation cost and risk-in-cost of smart energy hub systems. In the first stage, the obtained results demonstrate that using DR accompanied by DRC yields the optimum operation strategy where the operation cost and risk are maintained at an acceptable point. Further, simulation results suggest that FDI attacks can deviate the operation cost and risk by a substantial amount, and the proposed Monte Carlo-based detection/correction method mitigates almost 79 percent of the false data while keeping the convergence speed in an acceptable range. Moreover, modeling the participation rate of the demand response program by the Z- number approach increases the validity of the results and the applicability of the proposed model.

Congestion management for coordinated electricity and gas grids in the presence of multi-energy hubs: A risk-based optimal scheduling

Due to the development of cost-effective and high-efficient infrastructures in electricity and natural gas carriers, the need for optimal operation of multi-carrier energy systems is essential. On the other side, with the increasing penetration of renewable energy resources (RERs), and power demand, the congestion management (CM) of power systems is turning out to be a significant challenge for system operators. Motivated by these, this paper focuses on the congestion management problem for a multi-energy hub (MEH) system incorporated by RERs, flexible energy resources (FERs), and different energy conversion technologies to supply electrical, heat, and cooling demands, aiming to minimize the total operating costs. In current practice, the steady-state Weymouth equation and AC-power flow method for natural gas and electricity networks are implemented to investigate the interdependency of power and gas networks. The renewable energy generation variations are mitigated by the scenario-based stochastic framework. Also, to manage the risk associated with random variables, the downside risk constraint (DRC) model is employed to analyze the changes in the expected cost per several values of the risk parameters. The numerical results demonstrate that the proposed model can significantly utilize the energy hub’s flexibility to alleviate gas and power congestion while decreasing the total operation cost.

Decentralized energy management system of distributed energy resources as virtual power plant: Economic risk analysis via downside risk constraints technique

A virtual power plant (VPP), as a decentralized energy management system (EMS), can be a handy application to aggregate and schedule distributed energy resources (DER). The DERs can be dispatchable and non-dispatchable energy resources wishing to participate in the day-ahead (DA) electricity market. Therefore, they can provide the grid with higher reliability and safety margins. However, there are uncertainties in the market that VPP should manage and the associated risks to obtain robust scheduling in such a probabilistic environment. In this paper, risk-oriented stochastic optimization is proposed to investigate the market's uncertainties and obtain the maximum profit while ensuring the safety margins of the network. Downside risk constraints (DRC) are integrated into the model to cover the risks. So, the risk-based scheduling of VPP is obtained. The proposed risk-based framework is formulated as the mixed-integer linear programming (MILP) model, solved by the CPLEX solver of GAMS software. The proposed model is applied to an 18-bus test system to investigate the proposed risk-oriented stochastic optimization of VPP. Finally, the costs, revenues, and total profit are illustrated, compared, and analyzed in the model's risk-neutral (RN) and risk-averse (RA) states so that an appropriate decision can be made. Moving toward the RA strategy lowers the amount of generated power of the DG and the purchased power from the upstream grid, consequently leading to a lower amount of sold power by the VPP within the studied power grid. The analysis shows that the RA strategy decides with low risk by decreasing the cost and revenue of the VPP simultaneously. In this regard, the results indicate that the average profit of the VPP decreased by 8.62%, from $15407 to $14079 when decision-maker tends to take risk-averse decisions with zero risk.

Optimal management of an energy-water-carbon nexus employing carbon capturing and storing technology via downside risk constraint approach

The development of infrastructures in a sustainable manner ensures the preservation of human life. It means that societies should have constant access to clean energy and water resources. The focus on nexus planning and operation including renewable energy sources has covered the requirements for the development of sustainable integrated systems to provide secure energy and water while minimizing carbon footprint. However, the uncertainty of renewable energy resources challenges the operation of such integrated nexuses. In this regard, this paper proposes a risk-constrained operation of a sustainable energy-water nexus with the aim to reduce operational costs as well as carbon dioxide (CO2) emissions amount (t CO2). To supply the energy and water demands, a combination of renewable energy sources and desalination systems are considered. The downside risk constraint is proposed to investigate the impacts of risks of uncertainties associated with renewable energy production and electricity consumption as well as water demands. Electrical and water storage infrastructures are involved to increase the flexibility of scheduling and ensure affordability and sustainability. The problem is modeled as a mixed-integer programming problem resulting in optimal decision-making to manage the energy/water resources and storage systems to provide clean and safe energy and water with the minimum CO2 emission. For the emission sector, the carbon capturing and storing (CCS) technology is proposed to capture the CO2 defusing by the energy sector and resolve the carbon-related issues. To restrict carbon production, both CO2 taxes and cap policies are examined and the results showed that the latter has a considerable effect to incentivize the decision-maker to use CCS technology.

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%.

Risk constraint techno-economic assessment of photovoltaic-penetrated distribution network considering hydrogen vehicles in a multi-market environment

Challenges arising from increasing the penetration roof-top photovoltaic (PV) system are mostly related to the uncertainties concerned with PV generation units, which significantly impact the optimal operation of the power grid and may impose economic risks on the network operator. This article analyzes the real-time and day-ahead financial risk of a PV penetrated smart distribution grid with hydrogen-powered vehicles demands. The proposed approach models the uncertainties using a scenario-based method and evaluates the resultant risk of these uncertainties by utilizing the downside risk constraint (DSRC) approach. Compared to other risk-measures, DSRC method is a convex non-equilibrium approach and provides a closed-loop solution which is incorporated explicitly as a constraint in the problem. Besides, due to the linearity of the proposed DSRC method, there is no increased computational burden of the final risk-based model. The uncertainties involved in the model include PV generation, electrical demand, real-time market price, and behavior of hydrogen-powered vehicles. The studied test system is the standard IEEE 33 bus distribution network which 14 buses have installed PV with capacity of 50-150% of their peak load. The results validate a 100% reduction of expected downside risk (EDR), while the total cost is increased only by 3.8%.

A multi-objective risk-constrained approach for capturing and recycling carbon dioxide in multi-energy combined heat and power microgrids

In the last decade, the significant level of carbon emission in the atmosphere has increased the temperature of the Earth by 50% compared to the pre-industrial period. In recent years, considering renewable energy sources integrated with different energy systems such as cooling, heating, and power has become widespread around the globe as the ambition is to produce sustainable and clean energy. However, as the conventional methods for energy generation are highly reliable, their usage for supplying energy demands is inevitable. Therefore, in such renewable-based generation systems, the issues arising from uncertainties are the main challenges to the high penetration of these technologies. In this paper, the aim is to appraise such challenges in a multi-energy combined heat and power (CHP)-based microgrid integrated with power-to-gas (P-2-G) technology. In the proposed system, the downside risk constraint (DRC) approach as an effective risk management method is considered for risk assessment. The proposed model is a mixed integer linear program that is modeled as a multi-objective risk-based problem, for which the epsilon constraints method is used to achieve the optimal solutions for the objectives. According to the results, using P-2-G as an environmentally friendly tool can reduce approximately 55% of the emission cost. Further, the operating cost is increased nearly 13% to obtain a zero-risk condition for optimal management.

Supply Chain Performance with a Downside-Risk-Averse Retailer and Strategic Customers

Predicting future promotion information on markdowns, customers can maximize their utilities by deciding when to buy. With this strategic behavior, this paper investigates a downside-risk-averse retailer’s integrated stock and pricing problem using a single case study method. Analyzing effects of the downside risk aversion and strategic customers is our purpose. By exploring a two-phase newsvendor model with a retailer selling to strategic customers, our work determines the downside-risk-averse retailer’s equilibrium ordering level and selling price. On this basis, effects of the downside risk aversion and the strategic behavior on the retailer’s optimum decisions and profit are analyzed. We find that the reverse effect of the strategic behavior can be mitigated by the retailer’s downside risk constraint. We also extend the model to a decentralized supply chain case. It is found that a low (high) downside risk aversion would mean that the supply chain profit in the decentralized case can (cannot) dominate the centralized under some (any) wholesale price contracts when customers are strategic. In addition, for different risk aversions, we also construct contracts to optimize the supply chain profit. Our results will provide reference evidence of making operational management decisions for the downside-risk-averse retailer in the case of strategic customers.

RETRACTED: Risk-involved modeling of electrical and thermal energy storage systems in smart apartment building utilizing downside risk constraints approach

RETRACTED: Risk-involved modeling of electrical and thermal energy storage systems in smart apartment building utilizing downside risk constraints approach

RETRACTED: Risk assessment of residential home in the presence of electrical, thermal and water storage systems via downside risk constraint method

RETRACTED: Risk assessment of residential home in the presence of electrical, thermal and water storage systems via downside risk constraint method

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.

Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost.