Optimal Dispatching of Distributed Generators in an MV ... - IEEE Xplore

Optimal Dispatching of Distributed Generators in an MV Autonomous Micro-Grid to Minimize Operating Costs and Emissions S. Conti, R. Nicolosi, S.A. Rizzo D.I.E.E.S., University of Catania (Italy) {sconti,rnicolosi,sarizzo}@diees.unict.it

Abstract- The optimal control strategy for autonomous microgrids proposed in this paper highlights the possibility to minimize the overall micro-grid operating cost, minimizing at the same time the pollutants emissions and assuming that all the power made available by the renewable generators (photovoltaic and wind systems) and the storage systems is injected into the micro-grid. The proposed procedure is based on a hierarchical control architecture. The optimization procedure has been performed on a test micro-grid and verified by computer simulations. The numerical results show that the solutions found by the centralized optimization procedure can improve the micro-grid performances with respect to the ones that can be obtained from the generators local control.

I. INTRODUCTION The methodologies applied to manage and control distribution networks are undergoing continuous changing in order to make these networks “active” systems, which can be defined as distribution networks with the possibility of controlling a combination of Distributed Energy Resources (DER), such as generators, loads, and storage systems. The Distribution System Operator (DSO) has the possibility to manage electricity flows using a flexible network topology. DERs take some degree of responsibility for system support, which will depend on a suitable regulatory environment and connection agreements [1]. In other words, distribution networks become active as the decision-making and control functions can become distributed and the power flows can be bi-directional and controllable. In the perspective of realizing active distribution networks, the international research activity has highlighted the possibility to implement new network paradigms, such as autonomous and non-autonomous micro-grids [2], [3], even though the present regulation framework does not allow DG to supply autonomously portions of public networks. In the present paper, an optimal control strategy is proposed for autonomous micro-grids in order to minimize the overall operating cost, borne by the Micro-Grid Operator (MGO), supposed to be able to manage the generation systems. Also, it is assumed that the pollutants emissions are to be minimized and all the power made available by the renewable energy sources (RES) and the storage systems is assumed to be injected into the micro-grid.

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The paper proposes a hierarchical control based on two levels: - the low level control is decentralized and it is performed by the local controllers of the programmable generators (typically, the local controllers have to perform a primary and secondary droop control of voltage and frequency); - the high level control has to perform centrally an economic optimization of the operation of the micro-grid as a whole, minimizing at the same time the pollutants emissions. There are different methods to solve this kind of optimization problem, such as finding multiple ParetoOptimal solutions [4] or optimizing a single multi-objective function obtained by a weighted sum of the targets [5]. In the latter case, in order to normalize the two different objectives, minimum and maximum values for each of them are calculated a priori. The two normalized objectives are then combined in a scalar objective function to be minimized. The optimization procedure is implemented by a central control unit (CCU), which is part of a Micro-Grid Central Controller (MGCC). Measured micro-grid loads, powers generated by the non-programmable generation units (typically based on RES, such as photovoltaic and wind systems) and powers drawn from the storage systems are inputs provided to the CCU, which calculates the optimal setpoints for the programmable generators in terms of the required active and reactive power outputs that allow to supply the loads, while keeping the node voltages within the range specified by Norm EN 50160. As real-world optimization problems often exhibit multiple optima, i.e. the objective function is multimodal, the algorithm used for the solution of the proposed optimization problem is the SelfAdaptive Low-High Evaluations Evolutionary Algorithm (SALHE-EA) [6], because it is able to correctly identify the global optimum, as well as different local optima. Furthermore, it also provides the niche radius of each optimum, which can be considered as a measure of the variation of the objective function in the neighborhood of the optimum. The aforesaid features allow to overcome the performance of a standard optimal power flow. II. GENERALITIES ON AUTONOMOUS MICRO-GRIDS CONTROL Appropriate control systems for micro-grid local generators are required in order to allow a correct management during

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micro-grids temporary or normally autonomous operation. Generally, the control schemes are designed to allow each type of generator to operate in both grid-connected (nonautonomous micro-grid) and islanded (autonomous microgrid) modes. In particular, a grid-connected operating mode allows a PQ control on generators, as it is possible to dispatch active and reactive power without local voltage and frequency control, which is ensured by the main grid. On the other hand, when the micro-grid is disconnected from the rest of the network, it is necessary to switch to a different generator control mode, i.e. a Vf control, which has to ensure voltage and frequency stability to the islanded network [8]. Starting from the analysis of some works proposed by researchers involved in studying suitable control strategies for autonomous microgrids, a general classification can be proposed taking into account centralized [9]-[11], distributed [12]-[17] and hierarchical [18]-[21] architectures (even though sometimes the border line is not so sharp-cutting). Centralized Control - Control functions exercised on generators and loads are performed by a single controller, which receives all the required measures of the electrical quantities and sends command signals to all the micro-grid controlled units, e.g. reference values for active and reactive power dispatching. The advantages are having an overall vision and control on the network status thanks to the bidirectional communication infrastructure and having the possibility to perform an optimal generators dispatching. On the other hand, disadvantages are related to reliability issues, as the presence of a single controller and the need for an effective communication system can be critical aspects. Distributed Control - Control functions are performed by local controllers on each generator with no need for information exchange between them or with the MGCC. According to this concept, it is possible to realize primary and secondary voltage and frequency control on the ground of local measurements. For generators of different technologies, even for inverter-interfaced generator, it is possible to implement droop characteristics similar to the ones of synchronous generators. The advantages are a higher reliability of the control and communication system, while the main drawback is loosing the general view and the possibility of performing the optimization of economic or energy efficiency objectives. Hierarchical Control - This represents a control solution which can combine the advantages of the previous ones from the viewpoints of reliability and effectiveness. Most control actions can be undertaken locally, while simplified information are managed by a central controller which has to check the status of the whole system.

Typically, the local controllers have to perform a primary and secondary droop control of voltage and frequency. - the high level control has to centrally perform an economic optimization of the operation of the micro-grid as a whole, minimizing at the same time the pollutants emissions. As introduces before, the optimization problem is solved by the CCU, which has to establish a bi-directional communication with the local generators. A. Low Level Control The generation units are equipped with local controllers able to adjust the active and reactive power outputs according to droop characteristics as the ones reported in Fig. 1 and 2. Specifically, primary regulation moves the set points along the characteristics, according to the changes in load conditions, causing steady-state frequency and voltage errors (respectively, shown in Fig. 1, a and b): (1) Δf = k p ΔP ΔV = k q ΔQ

(2)

where kp and kq represent the negative slopes. In order to maintain adequate power quality levels, in terms of keeping voltage and frequency at nominal values, a secondary control is required on the generators output. This control action can be obtained by translating the droop characteristics, as shown in Fig.2, a and b. For given different values of P and Q, frequency and voltage can be kept at the nominal values by varying no-load frequency and voltage while keeping the slopes constant. The low level control can operate to ensure network stability without any support from the high level control. This means that any malfunctioning in the CCU or in the communication infrastructure does not involve misoperation of the local controllers. The contingency would only imply temporary lack of optimization in the micro-grid operation. f(p.u)

V(p.u)

f

1

V

1

Q

P

0

1

0

P(p.u)

1

a)

Q(p.u)

b)

Fig. 1. F-P and V-Q droop characteristics. Effect of primary droop control. f(Hz)

V(p.u)

1.10 1.05 fn

1

III. THE CONTROL STRATEGY This paper proposes a hierarchical control based on two control levels: - the low level control is decentralized and it is performed by the local controllers of the programmable DG units.

0

P2

P1

a)

P3 100

P(%)

0

Q2

Q1

Q3 100

b)

Fig. 2. F-P and V-Q droop characteristics. Effect of secondary droop control.

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Q(%)

Then, after local frequency and voltage control actions have taken place in order to maintain stable micro-grid operation, a further change in the generators set-points is possible in order to find the optimal power output which reduces generation costs and pollutants emissions, as explained in the following section. B. High Level Control The MGCC acquires the measures of the powers absorbed by loads, generated by RES and delivered by the storage systems. Then, the CCU finds the optimal programmable generators set-points according to the load conditions. Subsequently, the MGCC sends the optimal set-points to the local controllers in order to obtain the desired power output from the generators. The centralized procedure is based on the presence of a data base and a monitoring system. The data base contains the following information: - geometric, topological and electrical characteristics of the micro-grid, i.e. network nominal voltage, incidence matrix [22], branches impedances, lines current carrying capacity; - capability curves of the programmable generators; - generation costs, which include fuels costs and operating and maintaining costs (OMC), as a function of the active power output; - pollutants emissions as a function of the active power output; - weighting factors that quantify the relevance given to each objective functions. The monitoring system acquires and sends to the MGCC the following information: - powers absorbed by loads; - powers generated by non-programmable generators; - powers delivered by the storage systems; - powers generated by programmable generators set by the low level control. IV. THE DEVELOPED OPTIMIZATION PROBLEM As introduced before, the aim is to realize an optimal management of the micro-grid energy sources in order to minimize the overall microgrid operating cost borne by the MGO, while minimizing the pollutants emissions, assuming that all the power made available by the RES and the storage systems is injected into the micro-grid. The procedure is accomplished by solving a non-linear constrained multi-objective optimization problem whose solutions are the optimal power outputs required from the programmable generators. In order to normalize the two different objectives, maximum values of them were calculated for each programmable DG unit. The hourly operating cost objective function is: NDG

f1 = 1 −

∑ C k ( Pk ) ⋅ Pk k =1

C max

(3)

where: NDG is the number of DG units; is the active power (kW) generated by the k-th DG Pk unit; Ck(Pk) is the cost (€/kWh) of the active power of the k-th DG unit as a function of the active power output; Cmax is the maximum operating cost (€/h) obtained when all programmable generators produce their maximum power. Hence, f1 can be maximized to minimize the operating cost. The pollutants emissions objective function is: NDG

f 2 = 1−

∑

k =1

E k ( Pk ) E max

(4)

where: Ek(Pk) is the pollutants emission (ton/h) from the k-th DG unit as a function of the active power output; Emax is the maximum pollutants emission (ton/h) obtained when all programmable generators produce their maximum power. Hence, f2 can be maximized to minimize pollutants emissions. The two normalized functions described above are then combined in a single objective function to be maximized: f = λ1 ⋅ f1 + λ 2 ⋅ f 2 (5) where λ1 and λ2 are the weighting factors (equal to 0.5) that quantify the relevance given to each objective by the MGO. The optimization problem is subject to the following constraints: - the capability curves establish synchronous generators active and reactive power limits (Fig. 3); - balance between load, generation and power losses must be ensured; - standard EN 50160 establishes the upper and lower limits on node voltage variations ( 1.1 ⋅ Vnom and 0.9 ⋅ Vnom ); - the lines thermal limits establish the conductors current carrying capacity. V. TEST MICRO-GRID This section describes the MV micro-grid (represented in Fig. 4) used to test the proposed optimization procedure. The characteristics of the cable lines (conductor section 3x95mm2) are given in Table I, which shows the data related to length, resistance and reactance for each branch. The nominal apparent power of the loads is 800 kVA (cosφ=0.9, lag). The programmable generation units are diesel (PG0 and PG7) and natural gas (PG10) generators. Their nominal apparent powers (Anom) are, respectively, 3.75MVA, 3.125MVA and 5.625MVA. The power limits referred to their capability curves shown in Fig. 3 are reported in Table II. Table III shows the costs of programmable generators active power (€/kWh) as a function of some power outputs. Table IV shows the pollutants emissions from

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programmable generators (ton/h) as a function of the same power outputs. The information provided by Table III and IV have been used to obtain costs and emissions as functions of the variable power output by interpolation.

TABLE II P (KW) AND Q (KVAR) LIMIT VALUES FROM THE CAPABILITY CURVES Gen. PG0 PG7 PG10

Pmax 3000 2500 4500

Plim2 2700 2250 4050

Qmin -1200 -1000 -1800

Qlim1 1500 1250 2250

Qlim2 1950 1625 2925

Qmax 2550 2125 3825

P (p.u.) 1 Pmax

TABLE III COSTS OF PROGRAMMABLE GENERATORS ACTIVE POWER (€/KWH) Loading Gen. PG0 PG7 PG10

Plim2

Qmin

Qlim1 Qlim2 Qmax

Q (p.u.)

1

50% 0.264952 0.303559 0.257546

75% 0.232153 0.243382 0.220376

85% 0.241153 0.264582 0.235986

100% 0.300584 0.312598 0.270390

TABLE IV POLLUTANTS EMISSION FROM PROGRAMMABLE GENERATORS (TON/H)

Fig. 3. Schematic representation of the synchronous capability.

Loading PG0

Load 4

Gen.

Load 9

PG0 PG7 PG10

~ G

Branch 10

Branch 5 Branch 8

G ~

12

PG7

Load 7

Load 12

Branch 13

G ~ PG10

Branch 12

Load 11

Load 10

Branch 11

Branch 6

NPG5

Branch 7

NPG2

Load 3

Branch 3

Load 2

Load 6

Branch 2

Load 1

10

85% 8.64 7.2 11.16

100% 11.16 9.54 14.4

Load 13

NPG12

13

NPG13

The non-programmable generators which supply the considered micro-grid are five, connected to nodes 2, 5, 8, 12, 13 (respectively, identified as NPG2, NPG5, NPG8, NPG12, NPG13). Specifically, they represent one photovoltaic and four wind generators. The non-programmable generators can provide an overall power of 1900 kW. The photovoltaic generator has a peak power of 500 kWp and the produced energy can be totally injected into the micro-grid. The four wind generators have a rated power of 350 kW each. At the nodes where the wind generators are connected, storage systems with an overall power of 600 kW are also installed. The storage systems are mainly used to store the excess power produced by the wind generators during lowload hours.

8

Load 8

VI. OPTIMIZATION RESULTS NPG8

Fig. 4. Test Micro-Grid. TABLE I MICRO-GRID BRANCHES CHARACTERISTICS Branch 1 2 3 4 5 6 7 8 9 10 11 12 13

75% 6.84 5.58 9.18

Branch 9

Branch 1

Branch 4

50% 5.04 3.96 7.2

Length (km) 0.5 0.5 0.5 3 1 1 3 0.5 3 3 0.5 0.5 1

R (Ω) 0.0965 0.0965 0.0965 0.579 0.193 0.193 0.579 0.0965 0.579 0.579 0.0965 0.0965 0.193

X (Ω) 0.054978 0.054978 0.054978 0.329867 0.109956 0.109956 0.329867 0.054978 0.329867 0.329867 0.054978 0.054978 0.109956

This section presents the results of the optimization procedure performed under various operating conditions. The analysed cases, referred to as A, B and C, are all characterized by maximum load demand, but different power outputs from the sources (non-programmable generators and storage systems) are considered and assumed equal, respectively, to zero, 250kW and 500kW. The results of the optimization procedure are summarized in Tables V, VI and VII. These tables report: the average (10 trials) costs and emissions obtained for the optimal solution (Optimum) found by the CCU; a feasible, but non-optimal, solution (called NOS), which could be obtained by the microgrid low level control without any optimization. Specifically, we choose as NOS the feasible solution with lower fitness among all those evaluated by the SALHE-EA while working at each trial. The number of objective function evaluations in the stochastic section of SALHE-EA is set to 1000.

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In each considered case (A, B and C), the comparison between the optimum and the NOS, is carried out by assessing the percentage difference, concerning costs (Δc) and emissions (Δe), between the NOS and the optimal solution, according to the following expressions: E − EOPT C − COPT 100 (6) Δc = NOS 100 and Δe = NOS COPT E OPT where: C NOS is the cost for the NOS; COPT is the cost for the optimal solution; E NOS is the pollutants emissions for the NOS; EOPT is the pollutants emissions for the optimal solution. TABLE V AVERAGE COSTS AND EMISSIONS FOR OPTIMAL AND NON-OPTIMAL SOLUTIONS IN CASE A

particular, the costs reduction is 18.26% and the pollutants emissions abatement is 17.35%. The different improvements achieved by SALHE-EA in the various cases, highlight that the effectiveness and the advantage of the optimization depend on the micro-grid operating conditions when the optimization is performed. In order to show an example of the generators optimal dispatching values obtained by the SALHE-EA, Tables VIII, IX and X report the following results. Note that in the first column of Tables VIII, IX and X, Pi and Qi are, respectively, the active and reactive powers from the i-th programmable generator. TABLE VIII GENERATORS DISPATCHING, COSTS AND EMISSIONS FOR THE OPTIMAL SOLUTION AND THE NOS IN CASE A Optimum

NOS

PG0

2549.371

2976.542

QG0

1391.589

527.6128 1208.486

Optimum

NOS

Improvement

Costs (€/h)

2152.879

2439.192

13.30%

PG7

1908.52

Emissions (ton/h)

27.303

29.082

6.52%

QG7

1049.115

1824.169

PG10

4189.597

4468.097

QG10

1749.163

1841.295

Costs (€/h)

2151.21

2453.19

TABLE VI AVERAGE COSTS AND EMISSIONS FOR OPTIMAL AND NON-OPTIMAL SOLUTIONS IN CASE B Optimum

NOS

Improvement

Costs (€/h)

1708.062

2115.523

23.86%

Emissions (ton/h)

21.350

26.391

23.61%

Emissions (ton/h) Fitness

Emissions (ton/h)

104.76

0.239594

0.16245

TABLE IX GENERATORS DISPATCHING, COSTS AND EMISSIONS FOR THE OPTIMAL SOLUTION AND THE NOS IN CASE B PG0

TABLE VII AVERAGE COSTS AND EMISSIONS FOR OPTIMAL AND NON-OPTIMAL SOLUTIONS IN CASE C

Costs (€/h)

98.42

Optimum

NOS

2146.648

2970.64

QG0

1373.894

1106.706

PG7

1874.518

2445.912

Optimum

NOS

Improvement

QG7

1079.03

57.59532

1491.221

1763.547

18.26%

PG10

3374.226

1990.195

18.041

21.171

17.35%

QG10

1735.748

3030.839

Costs (€/h)

1708.05

2141.62

The aim of the comparison is to highlight the average advantage that can be achieved thanks to the optimization procedure with respect to the NOS in terms of both costs and pollutants emissions. Table V shows the results in Case A. The comparison between the optimum provided by the SALHE-EA and the NOS highlights that the optimization allows a costs reduction (Δc) of 13.3% and a pollutants emissions abatement (Δe) of 6.52%. The results obtained in Case B are shown in Table VI . The optimum found by the optimizer brings about a significant advantage in both the objectives. In detail, it allows a strong costs reduction (Δc) of 23.86% and a pollutants emissions abatement (Δe) of 23.61%. Table VII shows the results in Case C. Once more, the optimum significantly outperforms the NOS, but, differently from the two previous cases, the optimum allows a reduction in the pollutants emissions greater than in the costs. In

Emissions (ton/h) Fitness

76.86

95.65

0.401387

0.252312

TABLE X GENERATORS DISPATCHING, COSTS AND EMISSIONS FOR THE OPTIMAL SOLUTION AND THE NOS IN CASE C PG0

NOS

1200.593

1422.45

QG0

1391.142

1813.037

PG7

1652.739

2490.905

QG7

1051.229

369.7062

PG10

3293.82

2235.751

QG10

1747.304

2008.044

Costs (€/h)

1490.43

1728.44

Emissions (ton/h) Fitness

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Optimum

65.09

77.11

0.485551

0.396907

Finally, the comparison between the optimum of Case A and the feasible NOS of Case B highlights the ability of SALHE-EA to achieve results (fitness=0.239594) comparable to the improvement in costs and emissions allowed by the use of RES and storage systems (fitness=0.252312) without optimization. Similar remarks can be made with reference to the comparison between the optimum of Case B and the NOS of Case C. VII.

[3] [4] [5] [6]

CONCLUSIONS

In this paper the problem of finding and optimal control strategy for MV temporary or permanently autonomous micro-grids supplied by programmable, non-programmable generators (based on RES) and storage systems has been dealt with. A hierarchical control system has been proposed, in order to account for both the technical issue of network stability and the need for obtaining an economic optimization of micro-grid operation, minimizing at the same time the pollutants emissions from programmable generators. The optimization procedure has been performed on a test MV micro-grid and the presented analysis has shown that the proposed control system allows a coordinated optimal dispatching of the various programmable power sources according to the variable conditions of micro-grid loads, RES and storage systems. The results have shown that the Self-Adaptive Low-High Evaluations Evolutionary Algorithm has been able to find optimal feasible solutions that can improve the performance of the micro-grid control. The effectiveness and the advantage of the optimization procedure depend on the micro-grid operating conditions when the optimization is performed. Though the use of RES and storage systems is itself a means to reduce operating costs and pollutants emissions, performing such an optimization procedure in a micro-grid with RES brings about additional advantages. Moreover, it has been highlighted how the optimization procedure based on SALHE-EA, applied to a micro-grid without RES, has the ability to achieve results comparable to the improvement in costs and emissions allowed by the use of RES and storage systems without optimization.

[7] [8] [9] [10]

[11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]

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