Risk Constraints Research Articles

Scalarized utility-based multi-asset risk measures

We introduce a risk measure that simultaneously allows to minimize hedging costs and maximize expected utility in the presence of a risk constraint. We call it the scalarized utility-based multi-asset (SUBMA) risk measure. For the SUBMA risk measure we state the following results: If the utility function has constant relative risk aversion and the risk constraint is coherent, then the SUBMA risk measure is coherent. In a one-period financial market setup, we present a sufficient condition for the SUBMA risk measure to be finite-valued. We derive results about the existence of optimal payoffs. Finally, we present a dual representation for a map that generalizes classical risk measures and has not been analyzed so far. In particular, it gives us the dual representation for the SUBMA risk measure.

Data-driven corporate growth: A dynamic financial modelling framework for strategic agility

This research aimed to develop a Dynamic Financial Growth Model (DFGM) to enhance corporate growth by promoting strategic agility through data-driven decision-making. The main objective was to optimize corporate value by integrating real-time data, dynamic decision-making, risk management, and scenario analysis. The research employed a mathematical modelling framework that combined predictive analytics, real options theory, and scenario-based optimization to represent dynamic corporate financial decisions. The numerical example demonstrated how the model adjusts strategic decisions in response to changes in market data and evaluates corporate value under optimistic, pessimistic, and baseline scenarios. The main results indicated that the DFGM is effective in optimizing corporate value by allowing for continuous adjustments and strategic flexibility, distinguishing itself from traditional static financial models that lack real-time adaptability. The findings highlighted the value of incorporating risk constraints and scenario analysis, resulting in a balanced approach that manages both growth and uncertainty. However, the study identified limitations, including the need for empirical validation, more complex predictive analytics, and accounting for behavioral factors affecting decision-making. The conclusion emphasizes that the DFGM provides an adaptable and data-driven framework that enhances corporate strategic agility, making it a valuable tool for managing growth in rapidly changing environments, while also suggesting future research to refine the model's practical application

The Contagion of Debt Default Risk in Energy Enterprises Considering Carbon Price Fluctuations

Under the constraints of low-carbon transformation goals, energy enterprises have significantly increased their debt default risk levels due to carbon price fluctuations. This article first analyzes the contagion mechanism of debt default risk among energy enterprises, and based on this, constructs a debt default risk contagion model among energy enterprises considering carbon price fluctuations, and then simulates and analyzes the evolution characteristics of debt default risk contagion among energy enterprises. The research results indicate that: (1) As the proportion of carbon emission cost increment and investor sentiment index increase, the stability of the debt network of energy enterprises strengthens. As the ratio of commercial credit among energy enterprises and influence of energy enterprises increase, the impact of debt risk gradually intensifies. (2) The investor sentiment index has a strengthening effect on the influence of energy enterprises, the proportion of commercial credit among energy enterprises, and the proportion of carbon emission cost increment. The commercial credit ratio between energy enterprises and its influence has a mutually reinforcing effect. (3) The investor sentiment index has suppressed debt default risk for various energy enterprises. The joint risk suppression effect of the proportion of carbon emission cost increment and the influence of energy enterprises in petroleum and petrochemical enterprises is more prominent. The joint risk constraint ability between the proportion of carbon emission cost increment and investor sentiment index in coal enterprises is stronger.

Dose tracking assessment for magnetic resonance guided adaptive radiotherapy of rectal cancers

BackgroundMagnetic resonance-guided adaptive radiotherapy (MRgART) at MR-Linac allows for plan optimisation on the MR-based synthetic CT (sCT) images, adjusting the target and organs at risk according to the patient’s daily anatomy. Conversely, conventional linac image-guided radiotherapy (IGRT) involves rigid realignment of regions of interest to the daily anatomy, followed by the delivery of the reference computed tomography (CT) plan. This study aims to evaluate the effectiveness of MRgART versus IGRT for rectal cancer patients undergoing short-course radiotherapy, while also assessing the dose accumulation process to support the findings and determine its usefulness in enhancing treatment accuracy.MethodsNineteen rectal cancer patients treated with a 1.5 Tesla MR-Linac with a prescription dose of 25 Gy (5 Gy x 5) and undergoing daily adapted radiotherapy by plan optimization based on online MR-based sCT images, were included in this retrospective study. For each adapted plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document}), a second plan (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}) was generated by recalculating the reference CT plan on the daily MR-based sCT images after rigid registration with the reference CT images to simulate the IGRT workflow. Dosimetry of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{T}\ ext{P}}_{\ ext{a}\ ext{d}\ ext{a}\ ext{p}}$$\\end{document} and\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\\:{\ ext{T}\ ext{P}}_{\ ext{I}\ ext{G}\ ext{R}\ ext{T}}$$\\end{document}was compared for each fraction. Cumulative doses on the first and last fractions were evaluated for both workflows. The dosimetry per single fraction and the cumulative doses were compared using dose-volume histogram parameters.ResultsNinety-five fractions delivered with MRgART were compared to corresponding simulated IGRT fractions. All MRgART fractions fulfilled the target clinical requirements. IGRT treatments did not meet the expected target coverage for 63 out of 94 fractions (67.0%), with 13 fractions showing a V95 median point percentage decrease of 2.78% (range, 1.65-4.16%), and 55 fractions exceeding the V107% threshold with a median value of 15.4 cc (range, 6.0-43.8 cc). For the bladder, the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{15\ ext{c}\ ext{c}}$$\\end{document} values were 18.18 Gy for the adaptive fractions and 19.60 Gy for the IGRT fractions. Similarly the median \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\ ext{D}}_{5\ ext{c}\ ext{c}}$$\\end{document} values for the small bowel were 23.40 Gy and 25.69 Gy, respectively. No statistically significant differences were observed in the doses accumulated on the first or last fraction for the adaptive workflow, with results consistent with the single adaptive fractions. In contrast, accumulated doses in the IGRT workflow showed significant variations mitigating the high dose constraint, nevertheless, more than half of the patients still did not meet clinical requirements.ConclusionsMRgART for short-course rectal cancer treatments ensures that the dose delivered matches each fraction of the planned dose and the results are confirmed by the dose accumulation process, which therefore seems redundant. In contrast, IGRT may lead to target dose discrepancies and non-compliance with organs at risk constraints and dose accumulation can still highlight notable dosimetric differences.

Mean-field backward stochastic differential equations with mean reflection and nonlinear resistance

The present paper is devoted to the study of the well-posedness of mean field BSDEs with mean reflection and nonlinear resistance. By the contraction mapping argument, we first prove that the mean-field BSDE with mean reflection and nonlinear resistance admits a unique deterministic flat local solution on a small time interval. Moreover, we build the global solution by introducing a two-step approach, which is a combination of stitching method and fixed point method. We further provide an application to the super-hedging problem with risk constraint.

Optimal Investment Strategy for DC Pension Plan with Stochastic Salary and Value at Risk Constraint in Stochastic Volatility Model

This paper studies the optimal asset allocation problem of a defined contribution (DC) pension plan with a stochastic salary and value under a constraint within a stochastic volatility model. It is assumed that the financial market contains a risk-free asset and a risky asset whose price process satisfies the Stein–Stein stochastic volatility model. To comply with regulatory standards and offer a risk management tool, we integrate the dynamic versions of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR), and worst-case CVaR (wcCVaR) constraints into the DC pension fund management model. The salary is assumed to be stochastic and characterized by geometric Brownian motion. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. By using the Lagrange multiplier method and the dynamic programming method to maximize the constant absolute risk aversion (CARA) utility of terminal wealth, we obtain closed-form expressions of optimal investment strategies with and without a VaR constraint. Several numerical examples are provided to illustrate the impact of a dynamic VaR/CVaR/wcCVaR constraint and other parameters on the optimal strategy.

Financial Hedging of Operational Risk Constraints: A General Framework

We consider the operation of a non-financial corporation and present a novel risk-management framework that supports the joint optimization of the firm’s operational and financial decisions. Specifically, we study the problem of maximizing the firm’s operating profits when these profits depend on movements in the financial markets, and where the decision maker—the firm’s manager—is risk averse. Critically, our framework (i) imposes risk aversion through a generic class of risk constraints, and (ii) considers explicitly the differences that can exist between the shareholders’ and manager’s beliefs about future market risks. Together, these aspects allow us to remove speculation so that financial instruments can only be used for hedging purposes. We analyze our problem in an incomplete financial market setting and consider different informational assumptions. Within this framework, we characterize the optimal financial hedging strategy so that the problem reduces to a constrained maximization over operational policies. Our results show how financial hedging can be effectively used to expand the set of admissible operating policies, and identify conditions under which the risk constraint can be completely ignored when solving for an optimal operating policy. We illustrate our methodology in the context of an exporter firm which faces exchange rate risk in trading.

Dosimetric comparison of ZAP-X, Gamma Knife, and CyberKnife stereotactic radiosurgery for single brain metastasis

PurposeTo evaluate the dosimetric characteristics of ZAP-X stereotactic radiosurgery (SRS) for single brain metastasis by comparing with two mature SRS platforms.MethodsThirteen patients with single brain metastasis treated with CyberKnife (CK) G4 were selected retrospectively. The prescription dose for the planning target volume (PTV) was 18–24 Gy for 1–3 fractions. The PTV volume ranged from 0.44 to 11.52 cc.Treatment plans of thirteen patients were replanned using the ZAP-X plan system and the Gamma Knife (GK) ICON plan system with the same prescription dose and organs at risk (OARs) constraints. The prescription dose of PTV was normalized to 70% for both ZAP-X and CK, while it was 50% for GK. The dosimetric parameters of three groups included the plan characteristics (CI, GI, GSI, beams, MUs, treatment time), PTV (D2, D95, D98, Dmin, Dmean, Coverage), brain tissue (volume of 100%-10% prescription dose irradiation V100%-V10%, Dmean) and other OARs (Dmax, Dmean),all of these were compared and evaluated. All data were read and analyzed with MIM Maestro. One-way ANOVA or a multisample Friedman rank sum test was performed, where p < 0.05 indicated significant differences.ResultsThe CI of GK was significantly lower than that of ZAP-X and CK. Regarding the mean value, ZAP-X had a lower GI and higher GSI, but there was no significant difference among the three groups. The MUs of ZAP-X were significantly lower than those of CK, and the mean value of the treatment time of ZAP-X was significantly shorter than that of CK. For PTV, the D95, D98, and target coverage of CK were higher, while the mean of Dmin of GK was significantly lower than that of CK and ZAP-X. For brain tissue, ZAP-X showed a smaller volume from V100% to V20%; the statistical results of V60% and V50% showed a difference between ZAP-X and GK, while the V40% and V30% showed a significant difference between ZAP-X and the other two groups; V10% and Dmean indicated that GK was better. Excluding the Dmax of the brainstem, right optic nerve and optic chiasm, the mean value of all other OARs was less than 1 Gy. For the brainstem, GK and ZAP-X had better protection, especially at the maximum dose.ConclusionFor the SRS treating single brain metastasis, all three treatment devices, ZAP-X system, CyberKnife G4 system, and GammaKnife system, could meet clinical treatment requirements. The newly platform ZAP-X could provide a high-quality plan equivalent to or even better than CyberKnife and Gamma Knife, with ZAP-X presenting a certain dose advantage, especially with a more conformal dose distribution and better protection for brain tissue. As the ZAP-X systems get continuous improvements and upgrades, they may become a new SRS platform for the treatment of brain metastasis.

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.

Active Thermal Management Method for Improving Current Capability of Power Devices under Influence of Random Convection

The active thermal management (ATM) method can improve the current capability of power devices safely by utilizing the thermal limit of the device. However, the existing methods are not suitable for power devices affected by random convection. This is because the randomness of convection will lead to the uncertainty of increased current capability, which is unacceptable in most engineering applications. To overcome the shortcomings of existing ATM methods, this paper proposed a novel ATM method for power devices under the influence of random convection. In the proposed method, the short-time current capability of the power device is maximized according to the thermal inertia of the device and maximum load current. The continuous current capability of the power device is determined by a maximum continuous current that satisfies the over-temperature risk constraint of the device. To accurately estimate the maximum continuous current, an uncertainty model is presented for the convective thermal resistance of the power device based on wavelet packet transform and Markov chain. A series of experimental studies are carried out by taking a power MOSFET as an example. The experimental results show that the proposed method can safely improve the output performance of the power device without causing random fluctuations in the current capability.

MRI-Linac radiation therapy outcomes in patients with prostate, breast and other advanced malignancies.

e13811 Background: MR-guided radiation therapy offers an innovative approach, providing superior soft tissue contrast that enables clinicians to deliver higher doses to the target site with fewer fractions. The objective of the study is to assess objective response rates and adverse effects related to a community-based single-institution experience with the ViewRay MRIdian. Methods: An IRB-approved registrational trial was conducted at the Providence Cancer Institute starting May 2020, with data cutoff of September 2023. All patients treated underwent MR screening, and the target site(s) were treated using hypofractionated radiation therapy in ≤5 fractions. The primary endpoint was to assess adverse effects attributed to radiation therapy. Secondary measures included local recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), overall survival (OS), and change in prostate-specific antigen (PSA) for patients treated definitively for prostate cancer. The assessments were performed under the guidance of experienced radiation oncologists. Results: From May 2020 to September 2023, n=257 patients received treatment on the MRIdian with a median follow-up of 430 days [range 4-1221 days]. One and 2-year LRFS was 92% and 87%, and excluding prostate and breast cancer, 83% and 70%. Definitive prostatestereotactic body radiation therapy(SBRT) was delivered to 117 patients and significantly reduced PSA (mean difference -6.27, p&lt;0.0001), with no local failures to date. One and 2-year DMFS was 75% and 67%, though if excluding definitive prostate SBRT and partial breast irradiation, these were 48% and 33%. The OS rate at 1 and 2 years was 86% and 76%. Excluding prostate and breast cancer, the 1 and 2-year OS was 74% and 55%. Grade 3+ adverse events possibly or definitely attributable to radiation therapy were noted in 5% (n=13/257) of patients, including 2 possible treatment-associated deaths (bowel perforation following liver SBRT, and respiratory failure following lung SBRT and trastuzumab-deruxtecan systemic therapy). Conclusions: Our institutional experience with the MRIdian demonstrated high levels of local control, while overall survival was driven by out-of-field failures. Serious adverse events were infrequent, but more common in abdominal targets highlighting the need for careful planning and tight organs at risk (OAR) constraints, particularly if systemic therapy is delivered soon after radiation.

Tracking and optimizing toxic chemical exposure pathways through food trade: A case study in SCCPs contaminated seafood in China

Abstract Food safety is related to human health and sustainable development. International food trade poses food safety risks through the collateral transport of toxic chemicals that are detrimental to human health. Domestic interprovincial trade has similar effects within countries but has not been comprehensively investigated previously. Here we assessed the effects of interprovincial trade on food safety and human dietary exposure to short-chain chlorinated paraffins (SCCPs), a group of emerging persistent toxic chemicals, in seafood across China by synthesizing data from field observation and various models. Our findings indicate that there is a higher level of SCCPs exposure risk in coastal provinces compared to inland provinces. Approximately 70.3% of human exposure to SCCPs through seafood consumption in China was embodied in the interprovincial seafood trade in 2021. Specifically, the domestic trade led to a remarkable increase in SCCPs exposure in the coastal provinces in South China, attributable to low SCCPs pollution in these provinces and imported seafood from those provinces with high SCCPs pollution. In contrast, human exposure to SCCPs decreased in those coastal provinces in East China due to importing seafood from those provinces with low SCCPs concentrations. The interprovincial seafood trade routes were optimized by linear programming to minimize human exposure to SCCPs considering both shipping cost and health risk constraints. The optimized trade routes reduced the national per capita SCCPs exposure through seafood consumption by over 12%. This study highlights the importance of interprovincial food trade in the risk assessment of toxic chemicals.

Multi-Objective Optimization for Food Availability under Economic and Environmental Risk Constraints

Food security remains a critical global challenge, increasingly threatened by the adverse effects of climate change on agricultural productivity and food supply chains. Ensuring the stability, availability, and accessibility of food resources necessitates innovative strategies to assess and mitigate climate-related risks. This study presents a comprehensive analysis of the impact of climate change on global food systems, focusing on the risk assessment and optimization of food supply chains from the perspective of importers. Deploying the Analytical Hierarchy Process (AHP), this study evaluates climate change risks associated with seven different suppliers for three key crops, considering a range of factors, including surface temperature, arable land, water stress, and adaptation policies. Utilizing these assessments, a multi-objective optimization model is developed and solved using MATLAB (R2018a)’s Genetic Algorithm, aiming to identify optimal suppliers to meet Qatar’s food demand, with consideration of the economic, environmental, and risk factors. The findings underscore the importance of a comprehensive approach in managing food supply chains and offer insights to enhance the resilience and sustainability of global food systems amid climate uncertainties. This study contributes to the literature by applying AHP and multi-objective optimization in climate risk management within food systems, providing valuable perspectives for policymakers and stakeholders in the agricultural sector. Furthermore, the multi-objective optimization model analyzed three crop networks, yielding total costs of USD 16 million, USD 6 million, and USD 10 million for tomatoes, onions, and cucumbers, respectively, with associated CO2eq emissions and risk percentages. The findings reveal concentrated global vegetable markets, with major importers accounting for over 60% of imports, though the leading importers differ across crops, highlighting regional demand and production disparities, potentially impacting food security and supply chain resilience.

A multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints

AbstractThis study develops a multi‐stage stochastic model to forecast the issuance of green bonds using the Filtered Historical Simulation (FHS) method to identify the most cost‐effective conditions for issuing these bonds amid various risk factors. Drawing on historical yield data and financial metrics of corporate green bonds from December 2014 to June 2023, the model considers fluctuating elements such as risk probabilities, financial risks in worst‐case scenarios, and liquidity risks at upcoming issuance moments. Our findings reveal the model's effectiveness in pinpointing the lowest possible costs of issuing new green bond portfolios in the future, while also addressing expected financial risk, risk occurrence probability, and liquidity issues. The results provide issuers with the insights needed to accurately time the market, tailor bond maturities according to a corporation's future risk profile, and enhance liquidity management. Notably, our model indicates that refining the estimated probability of future risk occurrences can lead to significant savings in green bond issuance costs. This approach allows for adaptable bond issuance strategies, addresses inherent debt, and enables detailed risk management, offering substantial benefits for green enterprises navigating the complexities of future financial landscapes.

On the equivalence between Value-at-Risk- and Expected Shortfall-based risk measures in non-concave optimization

We study a non-concave optimization problem in which an insurance company maximizes the expected utility of the surplus under a risk-based regulatory constraint. The non-concavity does not stem from the utility function, but from non-linear functions related to the terminal wealth characterizing the surplus. For this problem, we consider four different prevalent risk constraints (Expected Shortfall, Expected Discounted Shortfall, Value-at-Risk, and Average Value-at-Risk), and investigate their effects on the optimal solution. Our main contributions are in obtaining an analytical solution under each of the four risk constraints in the form of the optimal terminal wealth. We show that the four risk constraints lead to the same optimal solution, which differs from previous conclusions obtained from the corresponding concave optimization problem under a risk constraint. Compared with the benchmark unconstrained utility maximization problem, all the four risk constraints effectively and equivalently reduce the set of zero terminal wealth, but do not fully eliminate this set, indicating the success and failure of the respective financial regulations.1

1741: Simultaneous integrated boost and organ at risk constraints in the APHRODITE trial [ISRCTN16158514]

1741: Simultaneous integrated boost and organ at risk constraints in the APHRODITE trial [ISRCTN16158514]

Fault prevention strategy for coupled networks of power grids and transportation with the participation of electric vehicles

The increasing number of electric vehicles (EVs) that are connected to the power grid has raised concerns about the security of both transportation and power grids. This paper presents a fault prevention strategy that involves the use of social networks to improve the performance of electric vehicle charging stations. Firstly, the goal of this strategy is to improve the electric vehicle charging station’s performance by developing an evolutionary game model that takes into account the various factors that affect the electric vehicle’s response and travel time. Secondly, a number of charging station sample data is generated and analyzed using a convolutional neural framework. The capacity of the stations is then calculated by taking into account the different incentive measures offered by the power grid.. Furthermore, an optimal power flow model considering fault risk constraints and charging and discharging capacity constraints is established in the power grid or power grid subsystem to adjust the generator output and charging station power to realize the safe dispatching of the power grid. The simulation example shows that this method can fully consider the influence of traffic networks in the participation of EVs in power grid fault prevention, which is more reasonable.

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 %.

Decentralized stochastic linear-quadratic optimal control with risk constraint and partial observation

This paper addresses a risk-constrained decentralized stochastic linear-quadratic optimal control problem with one remote controller and one local controller, where the risk constraint is posed on the cumulative state weighted variance in order to reduce the oscillation of system trajectory. In this model, local controller can only partially observe the system’s state, and sends the estimate of state to remote controller through an unreliable channel, whereas the channel from remote controller to local controller is perfect; here, the unreliable channel means the Bernoulli channel, that is, when data is transmitted on this channel, there is a certain probability of failure. For the considered constrained optimization problem with Bernoulli channel, we first punish the risk constraint into cost function through Lagrange multiplier method, and the resulting augmented cost function will include a quadratic mean-field term of state. In the sequel, for any but fixed multiplier, explicit solutions to finite-horizon and infinite-horizon mean-field decentralized linear-quadratic problems are derived together with necessary and sufficient condition on the mean-square stability of optimal system. Then, approach to find the optimal Lagrange multiplier is presented based on bisection method. Finally, two numerical examples are given to show the efficiency of the obtained results.

Robust returns ranking prediction and portfolio optimization for M6

The M6 competition aims to address challenging problems in stock returns ranking prediction and portfolio optimization. To tackle the volatility and low signal-to-noise ratio in the stock market, our team designs the overall solution from the perspective of robustness. On the task of returns ranking prediction, we present the MultiTask Deep Neural Network with Denoising Autoencoder Enhancement (MT-DNN-DAE), which incorporates the self-supervised learning of DAE and jointly optimizes the multi-task loss. Additionally, we propose Robust Feature Selection (RFS) to identify features with a high signal-to-noise ratio for DAE’s representation learning and construct a separate branch for important ID feature to prevent its information loss. Results show our solution is capable of accurately predicting returns ranking while maintaining generalization. On the task of portfolio optimization, a Differential Evolution algorithm is also presented to optimize asset allocation and maximize returns under risk constraints, demonstrating improved performance over traditional techniques. Taken together, these methods led to a 4th place global ranking in the M6 competition.