UPEC2010 31st Aug - 3rd Sept 2010
Multi-objective long term optimal dispatch of distributed energy resources in micro-grids Gioacchino Corso DIEET Università di Palermo
Maria Luisa Di Silvestre DIEET Università di Palermo
[email protected]
[email protected]
Mariano Giuseppe Ippolito DIEET Università di Palermo
[email protected]
Abstract- The amount of distributed generators in power distribution systems is evermore increasing, and the potential benefits and drawbacks are becoming clearer. The optimal management of Distributed Energy Resources (DER) in microgrids is essential to guarantee their best exploitation. Optimal operation allows to maximize their benefits, such as reduction of network energy losses, reduction of carbon emissions and to minimize the negative effects of a strong DER penetration, such as the increase of infrastructural investment and operational costs. Hence, DER management is a multiobjective problem in which many objectives of interest, often conflicting, need to be optimized simultaneously. In this paper, the problem of the 24-hours unit commitment of distributed generators supplying a LV micro-grid connected to the main grid is addressed. The model developed considers the management of storage systems concentrated at some of the grid buses. The considered objectives are: to minimize generators annual cost, carbon emissions of the overall system and line losses. The unknowns are: the hourly power production of DER and the hourly storage unit level. The results show that the multi-objective approach based on non domination provides a wide range of alternatives and allows a strong optimization of the considered objectives. Index Terms-- Multiobjective optimization, microgrids, optimal management, storage systems.
I.
INTRODUCTION
In most engineering problems, problems formulations including cost and efficiency seem obvious. Fields like electrical power distribution where market liberalization has created complexity, require multiple objectives optimization problems formulations. The electrical power distribution area in the last years has experienced an important reorganization towards active networks characterized by a high penetration of Distributed Generation Units, DGU, based on technologies such as internal combustion engines, small and micro gas turbines, fuel cells, photovoltaic and wind plants; these systems may include also storage units. The possibility to produce energy in a physically distributed fashion gives the idea of many interests requiring high quality and economic operation. This is why currently power distribution management problems can be formulated as multi-objective optimization problems. DGUs are electric generating units (in microgrids typically in the range of 3 kW to 200 kW), parallel to the electric utility or stand-alone, located within the electric distribution system at or near the end user. DGUs also involve power electronic
Eleonora Riva Sanseverino DIEET Università di Palermo
Gaetano Zizzo DIEET Università di Palermo
eleonora.rivasanseverino@ unipa.it
[email protected]
interfaces, as well as communications and control devices for efficient dispatch and operation of single generating units, multiple system packages, and aggregated blocks of power. New actors, such as ‘prosumers’ (productors and consumers, because they either produce electricity or even sell their load curtailment) enter the energy market. This is why instead of DGU, the points of energy exchange are called DER, Distributed energy Resouces. Complex optimal management problems including the optimal dispatch of DER can be, in particular, formulated for ‘microgrids’. Intrasystem cross-supply and communal management standards differentiate a microgrid from a group of independent but physically proximate small generators. In order to optimize the power flows in the lines, a microgrid is equipped with load controllers and microsources controllers, that are interfaces to control interruptible loads and microsources (active and reactive generation levels), and a microgrid central controller that promotes technical and economical operation and provides set points to load controllers and microsource controllers. The scientific and economic interest in microgrids is motivated by the possibility to implement on a large scale renewable energy sources, to limit green house gas emissions, also reducing the transmission power losses, and to delay or even prevent the construction of new energy infrastructures. The co-ordination of all these generators and loads is a quite challenging issue also requiring distributed intelligence applications; for this reason these modern distribution systems are also referred to as ‘smart grids’. In the technical literature few articles propose operational solutions for microgrids because of the need of interdisciplinary knowledge. Several strategies for optimal operation in the literature minimize operational costs and maximize quality in microgrids. In [1], the optimization is aimed at reducing the fuel consumption rate of the system while constraining it to fulfil the local energy demand (both electrical and thermal) and provide a certain minimum reserve power. In this work and in [2], the problem is treated as a single objective problem by neither considering the emission nor the operation and maintenance costs as well as no sold or purchased power to or from the main grid.
In [3] a control strategy for inverter based DGUs and a protection scheme are carried out and a coordination between them is proposed to control both voltage and frequency during islanded operation. Reference [4] propose an efficient control system for microgrids management. The control system manages both the transient and the steady state features of the electrical system. The application is devoted to the implementation on a small Low Voltage test facility at the CESI (Centro Elettrotecnico Sperimentale Italiano, Milan, Italy).
solutions. P is a locally optimal Pareto set, if for every member x in P, there exist no solution y in a small neighbourhood, which dominates every member in the set P. P is a global Pareto-optimal set, if there exist no solution in the search space, which dominates every member in the set P. From the above discussion, it is possible to point out that there are primarily two goals that a multi-criterion optimization algorithm must achieve: 1)
II.
OPTIMAL MANAGEMENT OF MICROGRIDS
Knowing the hourly upper and lower production limits of each DER and the hourly loading level of each bus of the electrical distribution network, through a suitable forecasting software, the objectives to be achieved are: • the minimization of the power losses; • the minimization of the overall production costs; • the minimization of the voltage drops. The unknowns of the problem are: • the hourly power productions of the controllable DER. The problem is dealt with using a well known multiobjective stochastic approaches: the Non-dominated sorting Genetic Algorithm II [5]. In what follows, the approach is briefly described. Then the above cited power dispatch problem is described through a multi-objective formulation. Finally, an application example on the CESI test facility is provided. A.
The optimization algorithm: NSGA-II The algorithm used for solving the optimization problem is the Non dominated Sorting Genetic Algorithm II, NSGA-II [5]. The concept of non-dominance is one of the basic concepts in multiobjective optimization. For a problem having more than one objective function to minimize (say, fj, j=1,…,m and m>1) any two multidimensional solutions x1 and x2 can have one or two possibilities: one dominates the other or none dominates the other. A solution x1 is said to dominate the other solution x2, if both the following conditions are true: a) The solution x1 is no worse than x2 in all objectives, fj(x1)≤fj(x2), for all j=1….m. b) The solution x1 is strictly better than x2 in at least one objective, or fj*(x1)
