Riccardo Minciardi

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.

Decentralized optimal control of Markov chains with a common past information set

Decentralized dynamic (closed-loop) optimal control strategies are sought for a class of finite state Markov decision processes, characterized by the sharing of a common past after k steps of delay. The control is considered over a finite time horizon, and it is shown that a nonclassical dynamic programming procedure can be applied, based on the existence of a sufficient statistic of constant dimension. Finally, the infinite horizon case is briefly discussed, in view of an extention of existing results on the minimization of average expected cost for the centralized and decentralized control of Markov chains.

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.

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.

A Multilevel Approach for the Optimal Control of Distributed Energy Resources and Storage

An approach is proposed to deal with distributed energy resources, renewables and storage devices connected to microgrids. Specifically, a multilevel architecture is introduced and evaluated for the following main purposes: to reduce the computational complexity, to deal with different decentralized microgrids, different decision makers, and multiple objectives. A two-level decision architecture based on a Model Predictive Control (MPC) scheme is presented, in which the upper decision level has the function of fixing the values of a certain set of parameters (reference values), by assuming a certain structure of the control strategies to be applied at the lower decision level. On the basis of such parameters, each decision maker at the lower level solves its own optimization problem by tracking the reference values provided by the upper level. The effectiveness of the proposed approach is demonstrated. The application of the proposed control architecture to a specific case study (Savona, Italy) is presented and discussed.

A decentralized optimal control scheme for route guidance in urban road networks

A feedback decentralized optimal approach for traffic control in urban networks is developed. The control structure is based on the minimization of an objective function corresponding to the difference among the travel times from each origin node to all the possible destination through the available route on the network. The problem solution is provided on a temporal horizon not specified. The adopted methodology foresees the use of three different algorithmic structures; the first one is able to define the dynamics of the transportation system, and has gotten with a macro model of traffic simulation; the second is used for estimating the travel times on the links, using Gallagers algorithm (1977), and the third defines the resolution of a linear problem of optimization subject to non linear constraints.

Deterministic timed event graphs for performance optimization of cyclic manufacturing processes

A model of a cyclic manufacturing system representable as a deterministic timed event graph is considered in this paper. It is possible to apply analytical techniques for performance evaluation of such systems. On this basis, a two-level optimization problem is considered. The higher level refers to the maximization of the system productivity with respect to the assignment of operations to machines, the lot sizes, and the service sequences at the various machines, assuming that unlimited work-in-progress and buffers of finite dimension are available. The lower level deals with the minimization of an aggregate cost taking into account the work-in-progress and the buffer dimensions.