Sustainability

Contributed session

Time Slot: Thursday Afternoon

Room: AUD_B

Chair: Silvia Anna Cordieri

The Effect of Low Emission Zones on the Routing of Electric and Fossil-Fuel Vehicles

Time: 15:40

Ornella Pisacane (Dipartimento di Ingegneria dell’Informazione, Università Politecnica delle Marche ), Bruglieri Maurizio, Çatay Bülent, Keskin Merve, Mancini Simona

Due to the recent concerns about the environment and climate change, some countries around the world are implementing policies and rules for promoting the use of Alternative Fuel Vehicles (AFVs), especially in road transport which is responsible for a significant percentage of the total GreenHouse Gas emissions of the transportation sector. Among AFVs, Electric Vehicles (EVs) can significantly contribute to reducing both noise and environmental pollution. Some countries have established restrictions on the utilization of fossil-fuel powered Traditional Vehicles (TVs) in urban areas such as Low Emission Zones (LEZs) where TVs are either banned from entering or required to pay a toll to enter for the first time. Instead, EVs can travel without any restriction. The aim of our work is supporting the transportation companies toward an ecological transition, providing them with optimization-based approaches suitable to assist them in the process of replacing/integrating their traditional fleet. In fact, we address the problem of efficiently routing a mixed fleet comprising both TVs and EVs based at a common depot, for serving a set of customers at minimum total operating cost. We refer to it as the Mixed Fleet Vehicle Routing Problem with LEZ (MFVRP-LEZ). The operating costs include the cost of electricity recharged to EVs both at the depot and en-route, the fuel cost of TVs, and the tolls paid by TVs for entering the LEZ. Each vehicle starts its tour from the depot, serves a subset of customers, and returns to the depot before the end of its shift. Moreover, we assume that each customer must be served within a specified time window and an EV can recharge (also partially) en-route. We mathematically formulate MFVRP-LEZ as an arc-based Mixed Integer Linear Program (MILP) by allowing multiple visits to a station without cloning it and by identifying a-priori the set of non-dominated stations between each pair of customers. We solve small-sized instances using CPLEX. For solving large-sized instances, we design an Adaptive Large Neighborhood Search (ALNS) that extends the set of moves, already proposed in the related literature, with problem-specific new mechanisms. The experimental campaign is carried out on a set of benchmark instances of varying sizes derived from those already presented in the related literature by adding LEZs and arcs avoiding LEZs. Numerical results validate the effectiveness of ALNS and provide managerial insights.

Integrated planning of multi-energy systems (PlaMES): a comprehensive modelling framework and decision support tool

Time: 16:00

Matteo Pozzi (OPTIT), Bischi Aldo, Gordini Angelo

The European Union’s commitment towards a carbon-neutral economy can only be accomplished by a synergistic implementation of measures where Renewable Energy Sources expansion, integration of different energy systems and Transmission/Distribution infrastructure development are calibrated to meet future (2050) energy needs. The high complexity of the task comes from problem dimension, where future national demand and supply must be managed taking into account the interconnection between electricity, gas, heat and mobility sectors, with an hourly granularity to take into account multi-energy coupling dynamics. The Horizon 2020 project PlaMES, currently in its last year, aims to determine the optimal target system, including the investment trade-off between different technologies, infrastructure configurations and emissions reductions that minimize overall system costs.Introducing the overall business challenge, further explored in its detailed modelling and resolution strategies by the academic partners of the project during the session. Particular focus will be devoted to the decision support tool, that must handle extremely large quantities of data to provide significant scenario management capability to perspective decision makers, be them Transmission System Operators designing optimal infrastructure plans or Country Planners identifying what technology mix guarantees effective future energy systems that meet the challenges of decarbonisation.

Transmission expansion planning for future European energy grid

Time: 16:20

Antonio Punzo (DEI, University of Bologna), Monaci Michele, Vigo Daniele

PlaMES is an Horizon 2020 project aimed at developing an integrated planning tool for multi-energy systems on a European scale, taking into account both the expansion of generation and storage technologies and the related infrastructure in an integrated manner, so as to deliver to the European Union’s COP21 commitments.The PlaMES architecture consists of six tools. In this talk we will introduce the module designed for solving the Transmission Expansion Planning (TEP), which aims at identifying cost-efficient expansion and congestion management measures to ensure the system security and reliability of future electrical transmission grids.Our modelling approach considers different expansion and reinforcement measures and yields to an integer linear programming formulation, which is solved by using an exact enumerative algorithm in which a Benders decomposition scheme is used to compute a dual bound. The method is enhanced by means of a metaheuristic algorithm, and is computationally tested on realistic instances of large size.

A decomposition approach for the Central Energy System planning

Time: 16:40

Silvia Anna Cordieri (DEI, University of Bologna), Monaci Michele, Paronuzzi Paolo, Vigo Daniele

The Central Energy System (CES) planning is a problem including both unit commitment problem and generation expansion planning, and it is formulated as a linear program.The system consists of a set of nodes, each one with a set of available energy production technologies, a set of time steps, and a transmission network topology. For each node and time step a demand is given, and the objective is to minimize the suom of operational and installation costs, while satisfying the demand and respecting the maximum limit on CO2 emissions.Given the scale at which the problem is treated, a heuristic algorithm exploiting the block-structure presented by the mathematical formulation of the problem has been designed. In particular, the procedure works in two distinct phases. In the first phase, the considered time-horizon is divided in a number of subperiods, each one defining a subproblem presenting the same characteristic of the original problem. Then, these subproblems are solved (possibly in parallel) and their solution are used to define the values of all the design variables (i.e., the amount of capacity for each technology installed at each node and the possible expansion of the network). After the design variables has been fixed, in the second phase of the algorithm, the same subproblems are solved again (possibly in parallel), this way operational variables are also defined and a feasible solution is found.