Laura Pimpinella

A model predictive control approach to the load shifting problem in a household equipped with an energy storage unit

This paper deals with the load shifting problem in a household equipped with smart appliances and an energy storage unit with conversion losses. The problem is faced by establishing an event driven Model Predictive Control framework aiming to meet the real life dynamics of a household and to keep low the impact of the control system on the total electric energy consumption. The proposed approach allows the consumer to minimize the daily energy cost in scenarios characterized by Time of Use tariffs and Demand Side Management, by dynamically evaluating the best time to run of the appliances and the optimal evolution of the battery level of charge. A proper set of realistic simulations validates the proposed approach, showing the relevance of the energy storage unit in the domestic load shifting architecture.

An event driven Smart Home Controller enabling cost effective use of electric energy and automated Demand Side Management

This paper proposes the design of a Smart Home Controller strategy providing efficient management of electric energy in a domestic environment. The problem is formalized as a binary linear programming problem, the output of which specifies the best time to run of Smart Household Appliances, under a Virtual Power Threshold constraint, taking into account the real power threshold and the forecast of consumption from not plannable loads. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here optimization of economic saving in case of multi-tariff contract and Demand Side Management have been discussed and simulated. Simulations have been performed on relevant test cases, based on real load profiles provided by the smart appliance manifacturer Electrolux S.p.A. and on energy tariffs suggested by the energy retailer Edison. Results provide a proof of concept about the consumers benefits coming from the use of local energy management systems and the relevance of automated Demand Side Management for the general target of efficient and cost effective operation of electric networks.

A feedback linearization based Wind turbine control system for ancillary services and standard steady state operation

In this paper the control problem of the wind turbine driven doubly fed induction generator (DFIG) is faced, both in wind park operator and system operator perspective. Two control schemes are proposed, based on feedback linearization theory for MIMO systems and PI controllers: the first one for simultaneous active and reactive power regulation, the second one for simultaneous propeller angular speed and reactive power regulation. They are shown to allow the deliver of ancillary services to the system operator and the maximization of wind park operator profitability respectively.

Network decomposition and multi-path routing optimal control

In this work we face the Routing problem defined as an optimal control problem, with control variables representing the percentages of each flow routed along the available paths (multi-path routing), and a set of cost functions corresponding to the two different objectives of traffic balancing and bottleneck load minimization. A key issue considered in this paper is the possibility of splitting the given network in sub-networks, each one controlled by a separate subset of variables. This of course designs a decomposition of the original control problem in a set of easier-to-be-dealt-with lower dimensional problems. Some simulation results validate the procedure. Copyright

Local energy management system: Control scheme and loads modeling

In this work we present a general control scheme for the management of an energy community and a load modeling approach. We adopt a multilevel control scheme in which the first level computes the energy consumption set point towards the second level and the generation planning towards the energy sources, the second level is in charge of local loads scheduling and micro-generation planning.

Generation Portfolio Optimization by NPV formulation, Monte Carlo Estimators and Genetic Algorithms

In this paper we present the Portfolio Optimization Problem in the electricity generation framework. We consider traditional and fully controllable energy sources together with wind source, strongly supported by economical benefits but exposed to intermittent generation volatility. A new formulation in terms of uNPV (unit Net Present Value) is proposed and analysed, due to the statistical uncertainty about parameters, we formalize the optimization problem in a probabilistic sense in terms of Monte Carlo Estimators and structured in terms of Risk Aversion factor. The optimization routine is implemented with a Genetic Algorithm.

Optimal Control Strategies for Multipath Routing: From Load Balancing to Bottleneck Link Management

In this work we face the Routing problem defined as an optimal control problem, with control variables representing the percentages of each flow routed along the available paths, and with a cost function which accounts for the distribution of traffic flows across the network resources (multipath routing). In particular, the scenario includes the load balancing problem already dealt with in a previous work (Bruni et al., 2010) as well as the bottleneck minimax control problem. The proposed approaches are then compared by evaluating the performances of a sample network.

A Cross-Layer Approach to Dynamic Bandwidth Allocation in Satellite Networks

This work presents an innovative cross-layer approach to dynamic bandwidth allocation (BoD) in Satellite DVB-RCS networks. The algorithm is based on the assumption that, by managing the traffic at IP level through interaction with MAC level, a meaningful reduction in packet loss can be achieved, thus resulting in better resource exploitation. The proposed mechanism has been embedded in a consolidated control scheme for dynamic bandwidth allocation ([23], [1]). The interaction consists in the computation of the exact amount of MAC cells to send to the air interface during the next frame; based on this computation, the proper number of IP packets are segmented, transmitted to the MAC layer and queued in the MAC buffers. In this way, a twofold result is obtained: 1) no duplication of the scheduling function, scheduling can be performed at IP layer only, and 2) avoidance of overflows of MAC buffers. Simulations results, obtained by Opnet®, confirm the effectiveness of the proposed approach.

A Dynamic OFDMA Oriented Resource Control for WiMAX Networks

This paper deals with the problem of Resource Control in a WiMAX scenario. The aim of Resource Control into manage and allocate available bandwidth among users in away to obtain high resource exploitation. The proposed approach regards the uplink communication direction between a Base Station (BS) that assigns available resources through a scheduling procedure OFDMA oriented with Quality of Service guaranties and some Subscriber Stations (SSs) that make requests. Our approach is made by a measurement based algorithm resident in the base station that computes the assignments taking into account the channel quality and guaranteeing Quality of Service requirements.