Restoration of an active MV distribution grid with a battery ESS: A real case study

In order to improve power system operation, Battery Energy Storage Systems (BESSs) have been installed in high voltage/medium voltage stations by Distribution System Operators (DSOs) around the world. Support for restoration of MV distribution networks after a blackout or HV interruption is among the possible new functionalities of BESSs. With the aim to improve quality of service, the present paper investigates whether a BESS, installed in the HV/MV substation, can improve the restoration process indicators of a distribution grid. As a case study, an actual active distribution network of e-distribuzione, the main Italian DSO, has been explored. The existing network is located in central Italy. It supplies two municipalities of approximately 10,000 inhabitants and includes renewable generation plants. Several configurations are considered, based on: the state of the grid at blackout time; the BESS state of charge; and the involvement of Dispersed Generation (DG) in the restoration process. Three restoration plans (RPs) have been defined, involving the BESS alone, or in coordination with DG. A MATLAB®/Simulink® program has been designed to simulate the restoration process in each configuration and restoration plan. The results show that the BESS improves restoration process quality indicators in different simulated configurations, allowing the operation in controlled island mode of parts of distribution grids, during interruptions or blackout conditions. The defined restoration plans set the priority and the sequence of controlled island operations of parts of the grid to ensure a safe and better restoration. In conclusion, the results demonstrate that a BESS can be a valuable element towards an improved restoration procedure

An event driven Smart Home Controller enabling consumer economic saving 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 an event driven 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. The optimization is performed each time the system is triggered by proper events, in order to tailor the controller action to the real life dynamics of an household. This problem formulation allows to analyze relevant scenarios from consumer and energy retailer point of view: here overload management, optimization of economic saving in case of Time of Use Tariff 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 manufacturer 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. © 2012 Elsevier Ltd

Dynamic Bandwidth Allocation in Satellite Networks

This work presents an adaptive control approach for queue-based Bandwidth-on-Demand (BoD) procedures in geostationary satellite networks. In queue-based protocols, the controller objective is to drive the buffer queue length to a certain target queue length. The proposed protocol is based on the construction of a certain number of models (MRs) that represents a particular statistical network behavior in a way to obtain the above mentioned reference queue length. The reference queue length is computed as a weighted sum of the outputs of the different MRs. By varying on-line the target queue length a trade-off between efficient exploiting of bandwidth and reduction of queuing delay is achieved

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. © 2010 Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

A Novel Approach to Generation Portfolio Optimization by using Genetic Algorithms and Stochastic Methods

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. Due to the statistical uncertainty about parameters, we formalize the optimization problem in a probabilistic sense and solve it by using Genetic Algorithms

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. © 2010 IEEE

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

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. © 2010 IEEE

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. © 2011 IEEE

Multipath routing by network decomposition and traffic balancing

In this work we face the routing task defined as an optimal control problem, with control variables representing the percentages of each flow routed along the available paths(multipath routing), and a cost function which accounts for balanced traffic flow across the network resources. A further issue considered in this paper is the possibility/opportunity 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, leading to separate minimum unbalancing indices which sum up to a value lower than the minimum global one achievable in the no decomposition case. Some numerical simulation results validate the procedure. Copyright © 2010 The authors