Roberto Sacile

Optimizing forest biomass exploitation for energy supply at a regional level.

A decision support system for forest biomass exploitation for energy production purposes is presented. In the proposed approach, geographic information system based techniques are integrated with mathematical programming methods to yield a comprehensive system that allows the formalisation of the problem, decision taking, and evaluation of effects. The aim of this work is to assess the possibility of biomass exploitation for both thermal and electric energy production in a given area, while relating this use to an efficient and sustainable management of the forests within the same territory. The decision support system allows for the locating of plants and the computing of their optimal sizing (defining which kind of energy is convenient to produce for the specific area), taking into account several aspects (economic, technical, regulatory, and social) and deciding how to plan biomass collection and harvesting. A case study applied to a small Italian mountain area is presented.

An environmentally sustainable decision model for urban solid waste management

The aim of this work is to present the structure and the application of a decision support system (DSS) designed to help decision makers of a municipality in the development of incineration, disposal, treatment and recycling integrated programs. Specifically, within a MSW management system, several treatment plants and facilities can generally be found: separators, plants for production of refuse derived fuel (RDF), incinerators with energy recovery, plants for treatment of organic material, and sanitary landfills. The main goal of the DSS is to plan the MSW management, defining the refuse flows that have to be sent to recycling or to different treatment or disposal plants, and suggesting the optimal number, the kinds, and the localization of the plants that have to be active. The DSS is based on a decision model that requires the solution of a constrained non-linear optimization problem, where some decision variables are binary and other ones are continuous. The objective function takes into account all possible economic costs, whereas constraints arise from technical, normative, and environmental issues. Specifically, pollution and impacts, induced by the overall solid waste management system, are considered through the formalization of constraints on incineration emissions and on negative effects produced by disposal or other particular treatments.

Solid waste management in urban areas: Development and application of a decision support system

A decision support system (DSS) developed to assist the planner in decisions concerning the overall management of solid waste at a municipal scale is described. The DSS allows to plan the optimal number of landfills and treatment plants, and to determine the optimal quantities and the characteristics of the refuse that has to be sent to treatment plants, to landfills and to recycling. The application of the DSS is based on the solution of a constrained non-linear optimization problem. Various classes of constraints have been introduced in the problem formulation, taking into account the regulations about the minimum requirements for recycling, incineration process requirements, sanitary landfill conservation, and mass balance. The cost function to be minimized includes recycling, transportation and maintenance costs. The DSS has been tested on the municipality of Genova, Italy, and the results obtained are presented.

A Dynamic Decision Model for the Real-Time Control of Hybrid Renewable Energy Production Systems

The use of renewable energy sources can reduce the greenhouse gas emissions and the dependence on fossil fuels. The main problem of the installations based on renewable energy is that electricity generation cannot be fully forecasted and may not follow the trend of the actual energy demand. Hybrid systems (including different subsystems such as renewable energy plants, energy storage systems based on hydrogen or dam water reservoirs) can help in improving the economic and environmental sustainability of renewable energy plants. In addition, hybrid systems may be used to satisfy other user demands (such as water supply or hydrogen for automotive use). However, their use should be optimized in order to fulfill the user demand in terms of energy or other needs. In this paper, a model representing an integrated hybrid system based on a mix of renewable energy generation/conversion technologies (e.g., electrolyzer, hydroelectric plant, pumping stations, wind turbines, fuel cell) is presented. The model includes an optimization problem for the control of the different ways to store energy. The goal is to satisfy the hourly variable electric, hydrogen, and water demands. A specific application area in Morocco is considered and the results obtained are discussed in detail.

Optimal Control in a Cooperative Network of Smart Power Grids

The possibility to store energy, to exchange power and information on demand and production among grids allows us to achieve an active distribution which is of major interest for cooperative smart power grids, that are grids which can forecast demand and production and are able to exchange power in order to enhance the quality of the service. In this paper, a model to support optimal decisions in a network of cooperative grids is formalized as an original discrete and centralized problem here defined as cooperative network of smart power grids (CNSPG) problem. In the CNSPG problem, the control variables are the instantaneous flows of power in the network of grids, which can be obtained from the solution of a linear quadratic Gaussian problem on a fixed time horizon. A simple case study showing the enhancement which may be obtained from the introduction of direct connections among microgrids according to a lattice network is shown and finally discussed.

Decentralized Control of the Power Flows in a Network of Smart Microgrids Modeled as a Team of Cooperative Agents

The focus of this paper is on the decentralized control of smart microgrids (SMGs), where each microgrid is modeled as an inventory system locally producing energy by wind/solar sources. The objective is to satisfy the internal demand, and to exchange power with its local energy storage technology, the main grid, and other similar microgrids of the region. The problem faced, within this context, is the optimization (minimization) of the costs of energy storage and power exchanged among SMGs. A decentralized control strategy is proposed, which allows the storage level in each microgrid to operate around a reference value by cooperatively sharing power between microgrids. A distributed control approach is presented where different agents, one for each microgrid and one for each power line, agree on a saddle point of a local function. The approach is based on the classical work of Arrow on convex optimization, which has seen renewed interest with its recent application to team theory and in its connection with the decomposition of feedback systems. An example is illustrated to show the practical use and the limitations of the method.

Coordinated Model Predictive-Based Power Flows Control in a Cooperative Network of Smart Microgrids

In this paper, a model predictive control (MPC) for the optimal power exchanges in a smart network of power microgrids (MGs) is presented. The main purpose is to present an innovative control strategy for a cluster of interconnected MGs to maximize the global benefits. A MPC-based algorithm is used to determine the scheduling of power exchanges among MGs, and the charge/discharge of each local storage system. The MPC algorithm requires information on power prices, power generation, and load forecasts. The MPC algorithm is tested through case studies with and without prediction errors on loads and renewable power production. The operation of single MGs is simulated to show the advantage of the proposed cooperative framework relative to the control of a single MG. The results demonstrate that the cooperation among MGs has significant advantages and benefits with respect to each single MG operation.

Resource Allocation in Integrated Preoperational and Operational Management of Natural Hazards

The management of natural hazards occurring over a territory entails two main phases: a preoperational-or pre-event-phase, whose objective is to relocate resources closer to sites characterized by the highest hazard, and an operational-during the event-phase, whose objective is to manage in real time the available resources by allocating them to sites where their intervention is needed. Obviously, the two phases are closely related, and demand a unified and integrated treatment. This work presents a unifying framework that integrates various decisional problems arising in the management of different kinds of natural hazards. The proposed approach, which is based on a mathematical programming formulation, can support the decisionmakers in the optimal resource allocation before (preoperational phase) and during (operational phase) an emergency due to natural hazard events. Different alternatives of modeling the resources and the territory are proposed and discussed according to their appropriateness in the preoperational and operational phases. The proposed approach can be applied to the management of any natural hazard and, from an integration perspective, may be particularly useful for risk management in civil protection operations. An application related to the management of wildfire hazard is presented.

Optimal Control of Power Flows and Energy Local Storages in a Network of Microgrids Modeled as a System of Systems

In this paper, a centralized control model for optimal management and operation of a smart network of microgrids (SNMs) is designed. The proposed control strategy considers grid interconnections for additional power exchanges. This paper is based on an original Linear Quadratic Gaussian (LQG) problem definition for the optimal control of power flows in a SNMs. The control strategy incorporates storage devices, various distributed energy resources, and loads. The objective function aims to minimize the power exchanges among microgrids (MGs), and to make each local energy storage system in a MG works around a proper optimal value. The proposed model is evaluated through a case study in the Savona district, Italy, consisting of four MGs that cooperate together under an SNMs connected to a main grid. The case study shows that the proposed approach can effectively cope with the aim to decrease the intermittencies effects of renewable energy sources, and to manage real-time burst in the residential local demands.

Optimal Control of a Network of Power Microgrids Using the Pontryagin's Minimum Principle

The optimal control of the power flows in a network of microgrids (MGs) is presented. The problem is solved using the mathematical formalization of the optimal control based on the Pontryagins minimum principle (PMP). The objective is to deliver an optimal control strategy for the minimization of the power flows among MGs, and to maintain the storage system operating around a given reference value. This study proposes an original formulation based on the PMP that may be viewed as a preliminary continuous time attempt to model and control the exchange of power in a network of MGs. Its main originality is the use and the exchange of information and forecast of energy production and consumption on the whole set of MGs, to improve the overall quality of the power management, and energy storage. A method based on the PMP is developed to solve the corresponding constrained optimal control problem in an almost exclusively analytical way and thus, to calculate the optimal control. To prove the viability of the proposed approach, an example has been solved for the case of four MGs collaborating in a network.