Stochastic gradient methods for energy saving and a correct management in complex water supply systems

The management optimization of complex multi-source and multi-demand water resource systems under a high uncertainty level has been a subject of interest in the research literature (Labadie, 2004; Cunha & Sousa, 2010; Yuan et al., 2016). In this context, energy saving in operation of water pumping plants and reduction of water deficit for users and activities are frequently conflicting issues. Dealing with these problems, the definition of optimal activation rules for emergency activation of pumping stations are a relevant topic recently treated in Lerma et al. (2015) and Napolitano et al. (2016). In this study we want to define a trade-off between costs and risks considering the minimization of water shortage damages and the pumping operative costs, under different hydrological scenarios occurrences possibilities. Consequently, optimization results should provide the water system Authorities with a robust information about the optimal activation rules considering a large set of generated scenarios of hydrologic inputs to reservoirs. Using synthetic series it is possible to take into account the climate change impacts and balance the rules while also considering future behavior under the risk of the occurrence of shortages and the cost of early warning procedures to avoid water scarcity, mainly related to activation of emergency water transfers. Thereafter, this problem has been faced considering an efficient optimization tool based on the Stochastic Gradient method (SQG), see Ermoliev & Wets (1988) and Gaivoronski (2005). Testing the effectiveness of this proposal, an application of the modelling approach has been developed in a water shortage prone area in South-Sardinia (Italy)

Optimization of complex simulation models with stochastic gradient methods

We describe the structure of stochastic optimization solver SQG (Stochastic QuasiGradient), which implements stochastic gradient methods for optimization of complex stochastic simulation models. The solver finds the equilibrium solution when the simulation model describes the system with several actors. The solver is parallelizable and it performs several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. We provide several complex examples with applications to water resources management, energy markets, pricing of services on social networks

Optimisation approach to target costing under uncertainty with application to ICT-service

Target costing is a modern approach applied during product development that defines cost targets for products and its components. These cost targets are driven by customer requirements and achievable revenues. The intention of this paper is the integration of target costing with modern concepts of modelling uncertainty and management of risk based on optimisation. Contrary to the traditional focus of target costing on cost targets, this paper prefers a strategy for achieving a target profit. Moreover, in this paper target costing is understood as a continuous process with incremental changes of cost drivers, product and component design as well as product prices. Therefore, the change in costs and profit with respect to aforementioned control parameters is modelled by linear approximations. Hence, improved decisions concerning design and prices are derived by linear programming models. In practice, information concerning product and component costs, demand or customer preferences are not given with certainty. Therefore, we apply a stochastic programming approach to manage the risk inherent in the target costing process. After a general presentation, we apply our approach to the provision of an information and communication technology service where the level of uncertainty is considerable

Optimization-based profitability management tool for cloud broker

This paper represents a model that supports the choice of efficient service portfolios at a cloud service broker. Among the many types of different cloud service brokers, we focus on a firm that offers service bundles that are composed from different services of different internet software providers. The necessary integration, aggregation, and customization of services can be time consuming and costly. Whenever the cloud broker can choose from many service combinations, but has limited human resources with critical time to market, it is essential to prioritize some of the service bundles, markets, services, and internet software providers. The purpose of this paper is to facilitate this kind of decision. Moreover, both the time and resources required for creating service offerings and the customers' demand for these service bundles are subject to uncertainty. Because of this uncertainty, a cloud broker needs to be guided to potential service portfolios that give the best trade‐off between risk and profitability. Our model helps the decision maker to identify efficient service portfolios, ie, service portfolios that for a given risk have the highest profitability or for a given profitability have the lowest risk. Our paper shows the application of this model to a cloud broker that mediates mainly software as a service bundled with mobile subscriptions for telephony (calling and messaging) and internet access. The model is inspired by the ideas from financial portfolio optimization and product‐mix decisions under scarce resources. The model corresponds to a linear stochastic optimization problem with an objective function that balances risk and profitability

Optimising Pumping Activation in Multi-Reservoir Water Supply Systems under Uncertainty with Stochastic Quasi-Gradient Methods

Under conditions of water scarcity, the energy saving in operation of water pumping plants and minimization of water deficit for users are frequently contrasting requirements, which should be considered when optimizing multi-reservoirs and multi-user water supply systems. This problem is characterised by a high uncertainty level in predicted water resources related to hydrologic input variability and water demand behaviour. We develop a mixed simulation-optimisation model using the stochastic quasi-gradient optimisation method to get robust pumping activation threshold values. This method allows solving complex problems, dealing efficiently with large size real cases with considerable number of data parameters and variables. The threshold values are chosen in terms of critical storage levels in the supply reservoirs. The optimal rules are obtained considering both historical and generated synthetic scenarios of hydrologic inputs to reservoirs. Hence, using synthetic series, we can analyse climate change impacts and optimise the activation rules considering future hydrologic conditions. The considered case-study is a multi-reservoir and multi-user water supply system in South Sardinia (Italy), characterised by Mediterranean climate and high annual variability in hydrological inputs to reservoirs. By applying the combined simulation and optimisation procedure, using the stochastic quasi-gradient method, a robust decision strategy in pumping activation was obtained

Scenario analysis for energy saving and management optimization in complex water supply systems

The pumping schedules optimization of complex water supply systems considering different hydrological scenarios occurrences is a significant issue when defining the activation of emergency and costly water transfers. As well known, this problem is characterized by a huge uncertainty level and it requires specific stochastic models in order to achieve optimized management rules. Particularly, treating the effectiveness of early warning and emergency transfers alleviating droughts, the operating costs required by pump stations activation stress the water systems’ Authorities to define a robust approach identifying these optimized rules. In this paper, this optimization procedure has been developed using the scenario analysis approach. The model allows identification of the optimal management rules by balancing the risk of water shortages under different hydrological scenarios and the energy costs due to the pumping stations operation. Scenario analysis optimization provides the water resource management Authority with information defining optimal activation thresholds for pumping stations in order to assure a water demand level fulfillment for users (irrigational, civil, industrial) and an energy saving policy. This optimization model has been implemented using the software GAMS, specifically designed for modelling mixed integer optimization problems. A model application has been developed to a real water supply system located in South-Sardinia (Italy) area. The obtained results define a cost-risk trade-off considering water shortage probability minimizing energy and operative costs