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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.

In recent years, as a result of emerging renewable energy markets, several developed regions have already launched Real‐Time Pricing (RTP) strategies for electricity markets. Establishing optimal pump operation for water companies in RTP electricity markets presents a challenging problem. In a RTP market, both positive and negative electricity prices are possible. These negative prices create economically attractive opportunities for Water Distribution System (WDS) to dispatch their energy consumption. On the other hand, excessively high prices may put WDS at risk of supply disruptions and reduced service levels. However, the continuous development of wind power and photovoltaics results in more volatile and unpredictable fluctuations in the price of renewable energy. The risk arising from uncertainty in electricity prices can lead to a significant increase in actual costs. To address this issue, this paper develops an a posteriori random forest (AP‐RF) approach to forecast the probability density function of electricity prices for the next day and provide a risk‐constrained pump scheduling method toward RTP electricity market. The experimental results demonstrate that the developed method effectively addresses the issue of increased costs caused by inaccurate electricity price forecasting. Plain Language Summary With the emergence of renewable energy markets in recent years, several developed regions have introduced Real‐Time Pricing (RTP) strategies for their electricity markets. This has created a difficult challenge for water companies seeking to establish the optimal pump operation in RTP markets. This study investigates the use of a risk‐constrained optimization scheduling approach for water distribution networks to mitigate the risks associated with inaccurate real‐time electricity price forecasting. Our proposed method is designed to reduce the costs associated with inaccurate electricity price prediction. Key Points A robust pump scheduling approach toward real‐time electricity price market is developed Developing a posteriori random forest algorithm to predict the probability density function of Real‐time electricity price Optimal scheduling with risk constraints is an effective approach to mitigating the risks associated with inaccurate electricity forecasting

Previous literature shows that prevalent risk measures such as value at risk or expected shortfall are ineffective to curb excessive risk-taking by a tail-risk-seeking trader with S-shaped utility function in the context of portfolio optimisation. However, these conclusions hold only when the constraints are static in the sense that the risk measure is just applied to the terminal portfolio value. In this paper, we consider a portfolio optimisation problem featuring S-shaped utility and a dynamic risk constraint which is imposed throughout the entire trading horizon. Provided that the risk control policy is sufficiently strict relative to the Sharpe ratio of the asset, the trader’s portfolio strategies and the resulting maximal expected utility can be effectively constrained by a dynamic risk measure. Finally, we argue that dynamic risk constraints might still be ineffective if the trader has access to a derivatives market.

In this paper, we study a new type of BSDE, where the distribution of the Y-component of the solution is required to satisfy an additional constraint, written in terms of the expectation of a loss function. This constraint is imposed at any deterministic time t and is typically weaker than the classical pointwise one associated to reflected BSDEs. Focusing on solutions (Y, Z, K) with deterministic K, we obtain the well-posedness of such equation, in the presence of a natural Skorokhod-type condition. Such condition indeed ensures the minimality of the enhanced solution, under an additional structural condition on the driver. Our results extend to the more general framework where the constraint is written in terms of a static risk measure on Y. In particular, we provide an application to the super-hedging of claims under running risk management constraint.

Dynamic portfolio optimization has a vast literature exploring different simplifications by virtue of computational tractability of the problem. Previous works provide solution methods considering unrealistic assumptions, such as no transactional costs, small number of assets, specific choices of utility functions and oversimplified price dynamics. Other more realistic strategies use heuristic solution approaches to obtain suitable investment policies. In this work, we propose a time-consistent risk-constrained dynamic portfolio optimization model with transactional costs and Markovian time-dependence. The proposed model is efficiently solved using a Markov chained stochastic dual dynamic programming algorithm. We impose one-period conditional value-at-risk constraints, arguing that it is reasonable to assume that an investor knows how much he is willing to lose in a given period. In contrast to dynamic risk measures as the objective function, our time-consistent model has relatively complete recourse and a straightforward lower bound, considering a maximization problem. We use the proposed model for approximately solving: (i) an illustrative problem with 3 assets and 1 factor with an autoregressive dynamic; (ii) a high-dimensional problem with 100 assets and 5 factors following a discrete Markov chain. In both cases, we empirically show that our approximate solution is near-optimal for the original problem and significantly outperforms selected (heuristic) benchmarks. To the best of our knowledge, this is the first systematic approach for solving realistic time-consistent risk-constrained dynamic asset allocation problems.

With the gradual increase of commercial banks and the expansion of their branches, the demand for cash transportation inflates sharply, bringing opportunities to the business development of Cash-In-Transit (CIT) sectors. However, the branches are often distributed in densely populated areas where traffic jams occur from time to time, which poses a severe challenge to the route planning of CIT vehicles. In addition, risk factors need to be considered during the optimization process because the goods transported belong to valuables. In order to effectively deal with the routing problem of CIT sectors, this paper established a bi-objective model and a goal programming model of Risk-Constrained Multi Depot Vehicle Routing Problems (RCMDVRPs) using real-time traffic data. Based on the traditional genetic algorithm, a Hybrid Genetic Algorithm with Intensification procedures (HGAI) is proposed to solve the goal programming model by using a three-level linked list structure to express chromosomes visually. Then, a new Self-constrained Hybrid Genetic Algorithm (SHGA) is designed for the bi-objective model. Besides, an online path updating strategy is developed to guide remote vehicles against time-dependent traffic flows. Finally, the HGAI is performed on benchmark instances to verify its accuracy. Experimental results of performance test show that the algorithm can achieve a gap of about 3% compared with the Best Known Result (BKR). The results of a case study also show that the two models and the corresponding algorithms are feasible and can be used to solve large-scale problems according to the special preferences and goals of decision-makers.

I document that US financial uncertainty shocks, measured by an increase in VIX, have a substantial impact on the output of emerging market economies (EMEs) without a material impact on US output during the last two decades. To understand this puzzling phenomenon, I propose a credit channel as a propagation mechanism of US financial uncertainty shocks to EMEs. I augment a boom-bust cycle model of EMEs by Schneider and Tornell (Rev Econ Stud 71(3):883–913 2004) with a portfolio choice model of constrained international investors. As international investors pull their money from EMEs—to satisfy their Value-at-Risk constraints—in response to financial uncertainty shocks, borrowing costs increase and domestic credit contracts. Higher borrowing costs and a decline in domestic credit, in turn, lead to a fall in investment in the non-tradable sector that causes a real depreciation via currency mismatch prevalent in EMEs and a decline in total output through sectoral linkages. The empirical regularity obtained by estimating structural VARs of 18 EMEs is consistent with the prediction of the model.

Dynamic stochastic optimization models provide a powerful tool to represent sequential decision-making processes. Typically, these models use statistical predictive methods to capture the structure of the underlying stochastic process without taking into consideration estimation errors and model misspecification. In this context, we propose a data-driven prescriptive analytics framework aiming to integrate the machine learning and dynamic optimization machinery in a consistent and efficient way to build a bridge from data to decisions. The proposed framework tackles a relevant class of dynamic decision problems comprising many important practical applications. The basic building blocks of our proposed framework are: (1) a Hidden Markov Model as a predictive (machine learning) method to represent uncertainty; and (2) a distributionally robust dynamic optimization model as a prescriptive method that takes into account estimation errors associated with the predictive model and allows for control of the risk associated with decisions. Moreover, we present an evaluation framework to assess out-of-sample performance in rolling horizon schemes. A complete case study on dynamic asset allocation illustrates the proposed framework showing superior out-of-sample performance against selected benchmarks. The numerical results show the practical importance and applicability of the proposed framework since it extracts valuable information from data to obtain robustified decisions with an empirical certificate of out-of-sample performance evaluation.

The water constraint on agricultural production receives growing concern with the increasingly sharp contradiction between demand and supply of water resources. How to mitigate and adapt to potential water constraint is one of the key issues for ensuring food security and achieving sustainable agriculture in the context of climate change. It has been suggested that adjustment and optimization of cropping systems could be an effective measure to improve water management and ensure food security. However, a knowledge gap still exists in how to quantify potential water constraint and how to select appropriate cropping systems. Here, we proposed a concept of water constraint risk and developed an approach for the evaluation of the water constraint risks for agricultural production by performing a case study in Daxing District, Beijing, China. The results show that, over the whole growth period, the order of the water constraint risks of crops from high to low was wheat, rice, broomcorn, foxtail millet, summer soybean, summer peanut, spring corn, and summer corn, and the order of the water constraint risks of the cropping systems from high to low was winter wheat-summer grain crops, rice, broomcorn, foxtail millet, and spring corn. Our results are consistent with the actual evolving process of cropping system. This indicates that our proposed method is practicable to adjust and optimize the cropping systems to mitigate and adapt to potential water risks. This study provides an insight into the adjustment and optimization of cropping systems under resource constraints.

Critical imbalances and threshold exceedances can trigger a disruption in a network of interdependent systems. An insignificant-at-first-glance shock can induce systemic risks with cascading catastrophic impacts. Systemic risks challenge traditional risk assessment and management approaches. These risks are shaped by systemic interactions, risk exposures, and decisions of various agents. The paper discusses the need for the two-stage stochastic optimization (STO) approach that enables the design of a robust portfolio of precautionary strategic and operational adaptive decisions that makes the interdependent systems flexible and robust with respect to risks of all kinds. We established a connection between the robust quantile-based non-smooth estimation problem in statistics and the two-stage non-smooth STO problem of robust strategic–adaptive decision-making. The coexistence of complementary strategic and adaptive decisions induces systemic risk aversion in the form of Value-at-Risk (VaR) quantile-based risk constraints. The two-stage robust decision-making is implemented into a large-scale Global Biosphere Management (GLOBIOM) model, showing that robust management of systemic risks can be addressed by solving a system of probabilistic security equations. Selected numerical results emphasize that a robust combination of interdependent strategic and adaptive solutions presents qualitatively new policy recommendations, if compared to a traditional scenario-by-scenario decision-making analysis.