An execution, monitoring and replanning approach

Energy 36 (2011) 3429e3436

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Energy journal homepage: www.elsevier.com/locate/energy

An execution, monitoring and replanning approach for optimal energy management in microgrids Eleonora Riva Sanseverino a, *, Maria Luisa Di Silvestre a, Mariano Giuseppe Ippolito a, Alessandra De Paola b, Giuseppe Lo Re b a b

DIEET, Università di Palermo, Edificio 9 Viale delle Scienze, 90128 Palermo, Italy DINFO, Università di Palermo, Edificio 6 Viale delle Scienze, 90128 Palermo, Italy

a r t i c l e i n f o

a b s t r a c t

Article history: Received 24 September 2010 Received in revised form 18 February 2011 Accepted 19 March 2011 Available online 22 April 2011

This work develops a new approach for optimal energy management of electrical distribution ‘smartgrids’. Optimality aims at improving sustainability through the minimization of carbon emissions and at reducing production costs and maximizing quality. Input data are the forecasted loads and productions from renewable generation units, output data are a set of control actions for the actuators. The considered electrical distribution system includes storage units that must be considered over a 24 h time interval, to consider an entire charge and discharge cycle. The objectives for the optimal management of distributed (renewables and not) generation are technical, economical and environmental. It is thus required to solve a multi-objective optimization problem over a 24 h time interval considering the uncertainty associated to weather conditions and loads profiles. The novelty of the proposed approach resides in considering the optimal scheduling of generation units an automatic planning process in a dynamic, non-deterministic and not fully observable environment, as it is, getting closer to actual conditions. The system proposed here is a planning and execution scheduler which allows the central controller to monitor the execution of a scheduling plan, interrupt the monitoring to input new information and repair the plan under execution every time interval. 2011 Elsevier Ltd. All rights reserved.

Keywords: Distributed energy resources management Multi-objective evolutionary optimization Microgrids

1. Background As an effect of all the activity around clean energy, communication technologies, smart meters, and electric vehicles, it is imperative to understand that the goal is to optimize the integration of such resources having the electrical distribution network as backbone. Optimizing the electrical grid management will thus be accomplished with intelligent software applications that can “predict, shape, and optimize” the network. The combination of interdisciplinary knowledge to develop intelligent, comprehensive, secure and integrated analytic-based applications is the key to achieving long-term success of the new developing idea of electrical distribution systems. The electrical power distribution area, in the last years, has indeed experienced an important reorganization towards active networks characterized by a high penetration of Distributed Energy Resources, DER, including Distributed Generation Units, * Corresponding author. Tel.: þ39 (0)916615262; fax: þ39 (0)91488452. E-mail addresses: [email protected] (E.R. Sanseverino), marialuisa. [email protected] (M.L. Di Silvestre), [email protected] (M.G. Ippolito), depaola@ dinfo.unipa.it (A. De Paola), [email protected] (G. Lo Re). 0360-5442/$ e see front matter 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2011.03.047

DGU, storage systems and controllable loads. DGUs are based both on renewable and not renewable technologies (internal combustion engines, small and micro gas turbines, fuel cells, photovoltaic and wind plants). The possibility to produce energy in a physically distributed fashion gives the idea of many interests requiring high quality and economical operation. This is why modern power distribution management problems can be formulated as multiobjective optimization issues. Complex optimal management problems can be, in particular, formulated for Microgrids. Microgrids [1] are low or medium voltage intelligent distribution networks comprising various DER which can be operated as interconnected to the main distribution grid or as islanded if disconnected from the main grid. From the grid’s point of view, a Microgrid can be regarded as a controlled entity within the power system that can be operated as a single aggregated load and as a small source of power or ancillary services supporting the network. Indeed, in order to optimize the power flows in the lines, a Microgrid is equipped with load controllers and micro source controllers, that are interfaces to control interruptible loads and micro sources (active and reactive generation levels), and a Microgrid central controller that promotes technical and economical operation and provides set points to load controllers and

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microsource controllers. Moreover, DGUs in Microgrids are electric generating units, typically in the range of 3 kWe200 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 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. Intra-system cross-supply and communal management standards differentiate a Microgrid from a group of independent but physically proximate small generators. In addition, Microgrids enhance local reliability, reduce emissions, due to the presence of renewable generators, improve power quality by supporting voltage and potentially lower the cost of energy supply. These goals can be achieved provided generation scheduling of power production from generation units is carried out over a daily to weekly time horizon while respecting various generator constraints and system constraints and considering renewable and loads variability. The objective function may include costs associated with energy production as well as other environmental and quality indices, such as limiting green house gas emissions, reducing the transmission power losses and delaying or even preventing the construction of new energy infrastructures. Differently from modern distribution systems including distributed generation and storage systems [2], in microgrids the problem of voltage and frequency regulation is of primary importance, since these systems may work in islanded configuration. The co-ordination of all these generating and loading units to meet demand, maximize quality and minimize emissions 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 this frame, what is most interesting is to create connection and to bridge ideas between proximate but quite different fields. The idea behind this work is that solution techniques that are suitable for robotics in dynamic environments can be used for Microgrids in uncertain conditions. The environment in which actuators should operate is indeed dynamic due to the presence of Renewable Energy Sources (RES) generation and variable loads. The depth of the decision tree through which the control actions must be planned and then taken is strongly dependent on the pricing horizons for energy (1 day-ahead market) and on the presence of certain storage systems which must be managed along an entire charge and discharge cycle (24 h). In the technical literature, the issue of optimal energy management in microgrids under uncertainty is dealt with considering the different aspects of the problem. Despite the microgrid mode of operation, i.e., 1) grid connected, 2) islanded (autonomous), or 3) transition between the two modes, the literature on the subject of power management in microgrids can be divided based on the level of regulation examined, i.e. 1) primary regulation, 2) secondary regulation, 3) tertiary regulation. An interesting general view of the problem can be found in Ref. [3] where a hierarchical architecture for control is presented with a special focus on autonomous operation. The primary control is typically based on the droop method; the secondary control allows the restoration of the deviations produced by the primary control finally the tertiary control manages overall objectives such as the power flow between the microgrid and the external electrical distribution system or the minimization of losses. Typically this latter optimization is carried out considering a 24 h time frame and is repeated every day. Most papers consider the primary/secondary regulation issues [4,5] while fewer papers focus on the problem of the optimal generation scheduling of DER in tertiary regulation: the problem is similar to that of traditional unit commitment for power

systems. In Ref. [6], 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. No storage systems are considered. In this work the problem is treated as a single objective issue by neither considering the emission nor the system’s operation and maintenance costs as well as no sold or purchased power to or from the main grid. The work in Ref. [7] considers the presence of storage systems, but as [6,8], it does not formulate the problem on a 24 h time frame and thus does not considers the issue of charge and discharge cycles and other pricing issues. The work in Ref. [9] develops the classical 24 h unit commitment problem using a three steps method. The algorithm is a hybrid method merging Lagrangian relaxation and Genetic algorithms. Cost minimization is the only considered objective. Same problem is faced in Ref. [10], but again the problem formulation nor the approach followed considers the issue of uncertainty. Research by Bertani et al. [11] proposes an efficient control system for Microgrids management considering a 24 h time frame and storage systems. In the latter, 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). None of the cited papers however deals with the problem of the uncertain environment in which the strategies are implemented (i.e. weather predictions over the considered 24 h time frame). As shown, 24 h dispatch is typically carried out every 24 h and the resulting set of commands is delivered to the Microsource Controllers and Load Controllers through a telecommunication system. In this way, forecasts about loads and weather conditions are affected by large errors and the economical and technical indices calculated as a result of such elaboration are affected by large errors. During operation, such errors are then corrected within fast regulation dynamics. The effects of the variability of environmental conditions can be indeed reduced through different approaches. In this paper, starting from the output of some data mining tools as loads and generation productions forecasting models, the optimal generators scheduling is deduced by identifying optimal real and reactive power dispatch among DER in Microgrids. The problem of uncertainty associated to a dynamic environment is considered adopting an Execution Monitoring and Replanning (EMR) approach [12]. The 24 h time frame is thus divided into T elementary time intervals, within which we can consider that the variability of weather conditions and loads is limited. Every elementary time interval, the problem can be formulated as follows: knowing the upper and lower production limits of each DER, the loading level of each bus of the electrical distribution network in the subsequent 24 h and the starting charging level of batteries, the objectives to be achieved are: the minimization of the 24 h energy losses; the minimization of the overall 24 h production costs; the minimization of 24 h carbon emissions. The unknowns of the problem are, at each time interval: the power injections at the nodes where the DER are installed; the storage levels of the batteries. The problem is dealt using a multi-objective stochastic approach: the non-dominated sorting genetic algorithm II [13] within an EMR cycle.


In what follows, the optimization algorithm is briefly outlined. Then a real world power distribution engineering application is described. After a brief review on the Microgrid concept and on the power dispatch problem, the multi-objective formulation is presented. Finally, application examples on the CESI test system [11] are provided.

field

State estimation

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24 hours load forecast 24 hours RES forecast

Day-ahead scheduler

DER control

2. Microgrids for electrical distribution

Fig. 2. Scheme of MicroGrid Central controller and dispatcher.

In this section, the authors will detail the practical problem of power generation optimal dispatch problem in Microgrids. In Fig. 1, the typical layout of a Microgrid is represented. As Fig. 1 shows, the Microgrid is supplied from the main Medium Voltage (MV) grid through an MV/LV transformer. The the MicroGrid Central controller (MGCC) is located downstream the transformer; the Microsource Controller (MC), and the Load Controller (LC), are respectively installed close to the DGUs and the loads. In the same figure, the Photovoltaic units are identified with PV, the Combined Heat and Power generating units are indicated with CHP, while the inverter is indicated with AC/DC (Alternating Current, Direct Current). The inverter interfaces the generating units with the electric grid and is able to change voltage module and displacement in order to adapt to the current operating requirements. The Load Controllers and Microsource controllers are usually implemented into inverters control logic. Every generating units produces LV electric power and can be connected to the grid through a transformer or a conditioning system. The following Fig. 2 shows the general architecture of the MicroGrid Central controller proposed by Bertani et al. [11]. As it can be observed, in the same unit the following functions are performed: - identification of the bus voltages all over the network, based on a set of redundant measures of electrical quantities (state estimation block); - load and renewable energy sources generation potential longterm forecasts (load and RES forecast blocks); - power generation dispatch (Day-ahead scheduler block). In Fig. 2, ‘field’ indicates the power distribution system. While RES indicates Renewable Energy Sources, comprising controllable loads, storage systems and generation units. The ‘state estimation’ block takes the available measures from the power distribution

Micro Turbine DC AC

MC LC

MC

MC

Load

LC

MGCC

HV

PV

Load LC

HV Network

DC AC

DC AC

Storage

Load Load

MV MC

DC AC

MC

CHP PV

LC DC AC

Load

Micro Turbine

Fig. 1. Typical Microgrid with its control devices.

system and turns them into reliable quantities to be processed for loads and renewables units output forecasts. Typically, forecasts are carried out using a Neural Networks-based approach for timeseries predictions. The block termed ‘24 h forecasts’ are referred to the prediction of loads and power production from renewable energy sources in the following 24 h. The ‘day-ahead scheduler’ calculates the active power set points (DER control actions) during the following day in order to optimize different technical, economical and environmental objectives. Finally the block named ‘DER control’ has as inputs the results of the ‘Dayahead scheduler’ and as outputs a set of control actions to be implemented by the Load Controllers and Microsource Controllers. Currently, the interest in the issue of managing Microgrids is quite high. The European community indeed is supporting the research in this field with a specific platform [14], and different calls within the Framework Program 7 (FP7). The latter being one of the most important EC initiatives for promoting research and playing a crucial role in reaching the goals of growth, competitiveness and employment within the European Union. In the following section, the mathematical formulation of the optimal dispatch problem is outlined.

2.1. 24-h power dispatch problem definition The technical and economical power dispatch within the dayahead scheduler consists in the identification of the optimal set points of the generating units, interruptible loads and storage systems in an electrical system along the subsequent 24 h. Typically the objectives are connected to the minimization of the production cost of these units, although also power quality and sustainability objectives can be considered within the optimization. Identifying the generated power scheduling of each unit giving the minimum production cost and the best operating indices requires the solution of the load flow problem, based on the knowledge of:the forecasted load at the network nodes; the forecasted generating upper limits for renewable energy sources; the size and type of DER in the system; the units production costs and environmental data. This work proposes a new approach for the day-ahead scheduler of a Microgrid. Each elementary time interval, based on the input data above listed, the scheduler elaborates a set of control signals that are sent to the DERs. In this paper, DERs include storage system and generation units and the elementary time interval is 1 h. These signals are turned by suitable transducers into actions over the regulating systems that modulate the power injections at the DERs.Consider an n-bus Microgrid system with: Nfix load or generation nodes with fixed forecasted real and reactive power demands or injections; NDG controllable DGU; and NSTO storage systems. The problem is that to identify the 24 2*NDG þ 24*NSTO real valued vector identifying the 24 h schedule of the operating points of the DERs in the network:

3432


h g;1 g;1 g;24 X ¼ P1g;1 ; .; pg;1 ; .; NDG ; .; Q1 ; .; QNDG ; .; P1 i g;24 g;24 g;24 1 24 ; .; E124 ; .; ESTO PNDG ; Q1 ; .; QNDG ; E11 ; .; ESTO

(1)

minimizing one or more among the following objectives: - joule energy losses Eloss in the system

Eloss ¼

24 X

X

h ¼ 1 i ¼ 1;nbr

2 Ri Ii;h

(2)

where nbr is the number of branches in the system, Ri is the ith branch resistance and Ii,h is the ith branch current at hour h; - fuel consumption costs CP

CP ¼

24 X

X

h ¼ 1 i ¼ 1;NDG

(3)

CPriðPgh Þ i

where CPri(Pigh) is the fuel consumption cost of the ith source at hour h, Pigh the power output of the ith source at hour h;

where n is the number of buses of the network; Pigh and Qigh are, respectively, the active and reactive power generated at the ith bus at hour h; PiLh and PiLh are, respectively, the active and reactive power demand at the ith bus at hour h; Vi is the absolute value of the voltage at the ith bus; di is the phase angle of the voltage phasor of the ith bus; Yii is the absolute value of the sum of the admittances of all the branches connected to the ith bus; Yij is the absolute value of the sum of the admittances of all the branches connecting the ith and the jth buses; wij is the phase angle of the admittance Yij. The system of equations (7) expresses the equality of the generated power (both real and reactive) with the sum of the power required by the loads and the power losses in the branches of the network (the terms given as a function of Vi and Yij). Therefore, the formulated problem is that to determine the operating points of the DGUs giving rise to a technicaleeconomical optimum as a compromise between minimum cost operation, high quality service and minimum emissions level. Minimum cost operation is ensured if the overall fuel consumption is minimum. The latter condition depends directly on the power generated by fuel-supplied DGUs. High quality service is attained if energy losses connected to conductors heating are kept as limited as possible.

- minimization of the CO2 emissions, Em

Em ¼

24 X

X

Emhi

(4)

h ¼ 1 i ¼ 1;NDG

where Emih are the CO2 emissions at hour h from the i-th DER. The typical constraints are: - upper and lower limits of the values of the controlled variables, namely the DG units power outputs, taking into account the required power reserves; g

gh

PJmin Pj

g

Pjmax

h ¼ 1; .; 24

j ¼ 1; .; NDG

g g gh g g Qjmin Pj Qj Qjmax pj h ¼ 1;.;24 j ¼ 1;.;NDG

(5) (6)

- voltage drops at network buses below 1% of the rated values. In (5) and (6): - respectively represent the active production at hour h at the j-th DG unit and the minimum and maximum limits of real power at the jth DG unit; - respectively represent the reactive production at hour h at the j-th DG unit and the minimum and maximum limits of the reactive power at the jth DG unit. The solution must also satisfy the constraint about power transfer limits in the network lines, this constraint is usually always satisfied in well designed networks, therefore it will not be considered. Energy losses Eloss and bus voltages can be calculated if the following highly non-linear load flow equations for each hour (loading level and production from DER) are solved:

Pigh ¼ PiLh þ Vi2 $Yii $cos wii þ

n X

Vi $Vj $Yij $cos dj di þ dij

gh

¼ QiLh þ Vi2 $Yii $sin wii þ

n X jsi

Vi $Vj $Yij $sin dj di þ dij

The algorithm used for solving the optimization problem is the Non dominated Sorting Genetic Algorithm II (NSGA-II) [13]. The concept of non-dominance is one of the basic concepts in multi-objective optimization. For a problem having more than one objective function to minimize (say, fj, j ¼ 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 ¼ 1,.,m. b) The solution x1 is strictly better than x2 in at least one objective, or fj*(x1) < fj*(x2) for at least one j* ˛ {1,.,m}. If any of the above conditions is violated, the solution x1 does not dominate solution x2. If x1 dominates the solution x2, it is also customary to write x2 is dominated by x1, or x1 is not dominated by x2, or, simply, among two solutions, x1 is the non-dominated solution. It is also important to observe that the concept of optimality in multi-objective optimization is related to a set of solutions, instead than to a single one. It is therefore possible to define Pareto local and global optimality for sets of 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. guide the search towards the global Pareto-optimal region; 2. maintain population diversity in the Pareto-optimal front.

jsi

Qi

3. The optimization algorithm

(7)

As NSGA [15], it divides the population in fronts of non-dominated solutions so that the search can be addressed towards


h=1

h=2

…….… ……

0.9

h = 24 mother

0.85 0.8

……

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father

starting hour for predictions

0.75

……

0.7

child

measured loading factor loading factor with error

0.65 0.6

Fig. 3. Crossover operator.

0.55

interesting areas of the search space, where the global Paretooptimal region is presumably located. NSGA-II varies from the NSGA in three main things. It is more efficient computationally, since the ranking of solutions is performed with an O(kN2) algorithm, instead of O(kN3), where k is the number of objectives and N is the population size; it significantly prevents the loss of good solutions once they have been found (elitism); it does not need any parameter specification. A Binary Tournament Selection operator is used to select the offspring population, whereas crossover and mutation operators remain as usual. Before selection is performed, the population is ranked on the basis of an individual’s non-domination level and, to allow the diversification, a crowding factor is calculated for each solution. Basically the NSGA-II works on a real coded string. Since we want the charge and discharge cycles of batteries to be positively ‘biased’ in order to meet different criteria, in the nodes where batteries are installed a non-uniform mutation operator can be applied. As it was said before, the bias can be imposed, if needed, to meet different criteria: - meeting load demand (charging during day and discharging during night in islanded systems); - limiting the number of manoeuvres; - limiting the charge and discharge power.

Field measures, 24 hours forecasts and initial state at time k

24 hours optimization

24 h - DER control actions

Implement first action

0.5 0.45 0.4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Fig. 5. Loading factor of the grid, measured versus predicted values.

In the other nodes, a uniform mutation has been applied through a small variation of the DER units injection within imposed limits. The crossover operator is described in Fig. 3. It is applied so as not to destroy the charge and discharge history of a storage system. Constraints have been handled using the constraint domination concept as proposed by Deb [16].

4. Managing uncertainty in Microgrids using an Execution Monitoring and Replanning approach Planning techniques based on Artificial Intelligence usually make simplifying assumptions about accessibility, dynamicity, and non-determinism of the surrounding world. However, real dynamic domains not always behave as planned. The interleaving of planning and execution can produce several advantages, such for instance, the capability of starting the execution plan before its completion or the acquisition of further information from the external world into the planner [12].

T=k

Field measures, 24 hours forecasts and new initial state

1.1

1 24 hours optimization



T=k+1

0.9

Field measures, 24 hours forecasts and new initial state

0.8

0.7 measured loading factor

24 hours optimization



T=k+2

loading factor with error

0.6

0.5 time starting hour for predictions

0.4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Fig. 4. Logical sequence of actions in the EMR approach for tertiary regulation in microgrids.

Fig. 6. Solar radiation, measured versus predicted values.

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LV

DC AC

MC

Storage

DC AC

MV MV Network

MC

PV

DC AC

Storage

MGCC

MC

MC

AC AC

Micro Turbine

DC AC

Storage Fig. 7. Test system.

Indeed, the effectiveness of classic planning methods depends on some hypotheses that could be not valid in highly dynamic domains. In particular, there is no guarantee that the execution of some selected actions produce the expected effects on the domain, or that some unforeseen physical processes do not perturb the system. In the Microgrid domain this uncertainty is mainly due to the forecast of loads and productions from renewable generation units; it has been demonstrated that the error of this kind of forecast grows linearly with the time [17]; this involves that, without any continuous acquisition of new information from the field, any action plan determined by any classical planning approach is doomed to miss the optimal solution. In the microgrids control area, the issue of interleaving among monitoring, planning, and acting has been treated only partially. The idea of the on-line changing of the action plan, on the basis of information coming

from the field, has been already presented in some works of the literature. In Ref. [18] a multi-agent architecture is presented for the optimization of Microgrid functioning in presence of different operators. Authors propose a Reinforcement Learning mechanism, modified in order to manage stochastic environment, such is the case of Microgrids. The idea behind this work seems very interesting, although the paper provided neither a detailed problem formulation nor any experimental evidence for the implementation. Moreover in the proposed approach the control actions were selected exclusively on the basis of a short-term rewarding, and no long-term planning was presented. ‘Monitoring and Acting’ may represent a valid approach for the on-line control of electrical systems. When more long-term planning is required, as effect of technical (presence of storage systems) and economical issues (free market environment), more accurate

Initial conditions for storage systems deduced by the 24 hours optimal dispatch between hours h-1 and h-1+24

Eh-11

Eh1

E241

E11

Eh+241

EhSTO

E24STO

E1STO

Eh+24STO

24-hours Optimal dispatch

Eh-1STO

storage systems

Fig. 8. EMR simulation.


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Table 1 Comparison of the two approaches. Static approach for dispatching

EMR approach for dispatching

Energy losses

Production cost

CO2 emissions

Energy losses

Production cost

CO2 emissions

0,27643 MWh/day

373,954 kV/day

1652 tons

0,246625 MWh/day

300,7908 kV/day

143,385 tons

planning tools are needed in order to account for weather variability in high penetration of renewable energy sources generation systems. Fig. 4 represents the logical sequence of actions in the proposed Execution Monitoring and Replanning architecture. Data collected from the field at time T ¼ k, after a suitable processing algorithm (state estimation), are used to run an optimal dispatch algorithm in the subsequent 24 h. The control actions relevant to the first hour are implemented and the procedure is started again. Of course the initial state of storage systems and other generation units is changed. The basic hypotheses under which the proposed approach is considered valid are that loads and weather forecasts are affected by an error that increases as the time horizon gets wider. This is proved by the relevant literature on the subject, which has been used to deduce the input data (weather and loads forecasts over a 24 h time horizon) for simulations. The considered variables to be predicted can indeed be modelled as nonstationary time-series. In particular, a multilayer feedforward neural network can be employed to forecast one-day ahead hourly load consumption of weekday, as suggested in [18]. The load consumption forecasting errors that are attained for domestic load profiles increase along time and range from 1.2% to 2.32%. We can assume to apply a similar model for solar radiation prediction [19] with errors ranging from 10% to 20%. In what follows, the error profiles depicted in Figs. 5 and 6 have thus been assumed for simulations.

5. Applications and results The application is devoted to prove the validity of the proposed EMR approach for optimal management of microgrids compared to what is currently done. A comparison of the current 24 h dispatch policy of the generation units and the EMR approach proposed by

the authors has been carried out. The test system is the LV test facility installed at the CESI. The network is depicted in Fig. 7. Details have been deduced from many reports available on the website [19]. The system has 15 nodes. The Distributed Energy Resources installed are: a CHP microturbine, 100 kWe, 167 kWth, 0.10003 V/kWh; a 42 kW/2 h Vanadium Redox Battery (rated output 42 kW, energy 84 kWh); a 64 kW/30 min high-temperature Zebra battery (rated output 64 kW, energy 32 kWh); a 100 kW/1 h Lead-acid battery (rated output 100 kW, energy 100 kWh); and two 31.3 kW hybrid photovoltaic system. No heating system has been considered in the applications. Batteries can charge and discharge with the same efficiency. The system is connected to the main grid and the cost for buying energy from the grid is the same as the selling price, 136 V/MWh; the emissions of the MV grid (MV/LV transformer: 630 kW) are considered to be 526.7 kg/MWh (source: ENEA, 2005). In order to test the efficiency of the EMR approach the following procedure has been followed: The 24-h optimization approach has been executed with starting time 1 am, the batteries totally discharged and the errors applied to production units and loads deduced following the approach described in Section 4. For the minimum cost solution, the obtained 24 h scheduling of the set points of the generation nodes have been saved. With the set points of the generation units deduced at the preceding step, the system has been simulated over a 24 h time interval with actual production and actual consumption at generation and at loading nodes; the values of the objectives (production cost, energy losses and CO2 emissions) have been saved. The EMR approach has been simulated in the following way: The 24-h optimization approach has been executed with changing starting time from 1 am to 12 pm, and the starting level of

static approach

0.09

EMR approach

0.08 0.07 0.06 0.05 0.04 0.03 0.02 0.01 0 1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Fig. 9. Batteries charge level along 24 h using the two approaches.

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the batteries at each time interval has been taken from the batteries level at the first hour in the minimum cost solution string of the preceding run; the errors applied to production units and loads have been deduced following the approach described in Section 4. For the minimum cost solution, the set points for the generation units obtained at the first hour have been saved; With the set points of the generation units deduced at the preceding step, the system has been simulated over a 24 h time interval with actual production and actual consumption at generation and at loading nodes; the values of the objectives (production cost, energy losses and CO2 emissions) have been saved. Fig. 8 explains the entire process for the EMR simulation. The average of minimum production cost solutions over 20 runs is summarized in Table 1. All the runs have been executed with 100 individuals and 100 iterations. As it can be observed, the management of the system even considering a limited variability of environmental conditions (10%e20% of random error can be quite limited for solar irradiation) is improved in terms of the considered technical, economical and environmental objectives. Energy losses are reduced of 12%, production cost is improved of 24% and emissions are reduced of 15%. The considered approach gives the manager of the network the possibility to dispatch the energy sources that are available in the system considering the weather variability and to adapt the actions accordingly with a more limited error. The suitability of the proposed approach is confirmed looking at the behavior of batteries along the 24 h using the two approaches. The attained charge and discharge cycles are comparable although smoother changes can be observed for the EMR approach, see Fig. 9. 6. Conclusions In this paper, an Execution Monitoring and Replanning approach has been used to solve the optimal 24 h energy generation dispatch problem in a smart grid. The approach followed shows that in the presence of high variability of the input data due to the presence of renewable energy sources the environment in which management is carried out cannot be considered stationary. For this reason, approaches commonly employed are no more efficient, causing a large adjustment of the operation set point during primary and secondary regulation. The system proposed here is a planning and execution system which allows the central controller to monitor the execution of a scheduling plan, interrupt the monitoring to input new information and repair the plan under execution every elementary time interval. The proposed control system performs an ‘execution-monitoring’ rather than simply testing the next action to execute. This way, the proposed system anticipates forthcoming situations and adjusts the plan in accordance. The replanning module is based on a heuristic multi-objective optimizer able to efficiently incorporate

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