Optimal Reconfiguration of Distribution Networks

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Optimal Reconfiguration of Distribution Networks According to the Microgrid Paradigm E. Ghiani, Member IEEE, S. Mocci, Member, IEEE, and F. Pilo, Member, IEEE

Abstract – In the last twenty years power systems observed important changes at the distribution level, due to the presence of distributed generation and the changing towards MV active networks, as well the institutional, regulatory and commercial reorganization. In this new scenario, among the various opportunities related to the use of DG, there is the opportunity of a partition of the distribution network in cells or microgrids. In order to find the optimal combination of microgrids, an algorithm able to deal with the reconfiguration of distribution systems is proposed. In the search of the best configuration among different combinations of microgrids, the algorithm uses a Sequential Monte Carlo simulation technique. The final structure found by the algorithm maximizes the sum of the savings in both the cost of energy purchasing and the cost of service interruptions. Index Terms -- Distributed Generation, Active Networks, Microgrid, Distribution Reliability.

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I. INTRODUCTION

HE modern society considers the electric energy an essential necessity, as demonstrated by the continuous increase of the consumptions. Hence, the need of a competitive market arises, in which energy negotiations and transactions can take place. The well-known environmental concerns, the sustainable development, and the electric market liberalization are posing to distribution engineers important challenges in order to achieve a compromise between economy and environmental needs. The current politic and economic international scenario, characterized by the petroleum and gas price volatility, by the intrinsic depletion of the primary fuels, and by an increasing demand, may give an important impulse to the exploitation of Renewable Energy Sources (RES), of Demand Side Response Actions, and finally to all those actions that may contribute to improve the overall energy efficiency. In this context, the Distributed Generation (DG), which is often based on RES and/or on the newest high-efficiency technologies, is destined to receive a rising interest and will play a significant role in the electric power system [1], [2], [3]. In order to favorite the integration of DG many technical problems have still to be solved and adequate operating and control systems have to be developed. Innovative network schemes and network management policies have been recently Susanna Mocci, Emilio Ghiani and Fabrizio Pilo are with the Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123,Cagliari, Italy (e-mail: [email protected]).

proposed [4] and they may be synthesized with the concept of active network and microgrid [3]. A microgrid is a small MV or LV distribution system with distributed generation sources (e.g. wind generators, microturbines, fuel cells, etc.) and thermal and electric loads. From a conceptual point of view the microgrid distribution structure may be radial or weakly meshed with the generation sources and loads distributed along the system. Microgrids can be connected to the main network or can operate autonomously, similar to power systems of physical islands: in this case, in presence of a failure in the MV grid the reliability and resilience can be improved due to the possible operation in an isolated mode [5], [6]. LV microgrids may be connected to the MV distribution network by means of a MV/LV transformer. To the distribution companies the microgrid can be thought as a controlled load of the power system. To the customer it can be designed to meet his special needs and provide additional benefits, such as improved power quality and reliability, increased efficiency through co-generation and local voltage support [3]. Definitely, the full integration of microgrids in the power system requires the specification of standard and requirements to facilitate the connection of DG, a widespread use of tailored information, communication, control, and protection systems for the innovative operation of distribution networks. The reorganization of the distribution system into microgrids can determine significant benefits to the customers, with the opportunity of reducing the energy bill and increasing the perceived reliability, to the distribution system operators, that may postpone or avoid investments or activate new business, and to the environment thanks to the exploitation of RES. In the paper an optimization algorithm able to individuate the subdivision of a given distribution network into an optimized number of sustainable microgrid is proposed. The procedure aims at determining the network subdivision in microgrids that customer benefits expressed by the reduction in the annual energy expenditures and the improvement of the service continuity. The method furnishes the best configuration of microgrids in an existing distribution system: the optimal arrangement is the one that maximizes the sum of the savings in both the cost of energy purchasing and the cost of service interruptions. The paper is organized as follows. Section II is devoted to the description of the reorganization of the distribution

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network according to the microgrid paradigm. The optimization problem tackled and the solution algorithm are described in Section III. Some simulation results obtained with the application of the procedure to real distribution networks are reported in Section IV. Finally, the conclusions are provided in section V.

quantify these benefits and appropriate regulatory measures. In the paper, the aspects concerning the cost of energy and the cost related to the interruptions are investigated. Moreover, the proposed algorithm strives to achieve the maximum exploitation of renewable energy sources taking benefit from the microgrid capabilities of controlling the operation of loads, generators and storage systems.

II. MICROGRIDS Technological advances and environmental pressures are driving the interconnection of renewable energy sources to the distribution network. However, the interconnection of large amounts of non traditional generation causes problems in a network designed for ‘conventional’ operation. The use of power electronics interfaces and the ‘bundling’ of generation and loads into microgrids or minigrids offers a potential solution. In essence, a microgrid consists of a combination of generation sources, loads and energy storage interfaced through fast acting power electronics. The aim of operating microgrid subsystems is to move away from considering DG as badly behaved system components, of which a limited amount can be tolerated in an area, to ‘good citizens’. A microgrid may take the form of shopping center, industrial park or college campus. To the utility, a microgrid is an electrical load that can be controlled in magnitude. The main advantages producible through a subdivision of existing portions of the distribution networks into interconnected microgrids autonomously operated are summarized in the following points. Environmental impact: the DG, in particular if based on renewable sources, is an excellent way to answer questions related to pollution emissions. Furthermore, this effect is amplified by the opportunity of controlling the generators at the microgrid level and this local management makes the customers more conscious of the importance of a responsible use of energy; Management and investments: the microgrids take advantage of all benefits related to the energy sources properly placed in proximity of loads. In fact, in a distribution network, DG can reduce power losses, remove some congestion and defer utility investment for the network enforcing; Reliability and Power Quality: the opportunity of using local generators during system faults can improve the reliability. However, it should be noted that the microgrids can also improve the power quality of the distribution system; Economic and market outlook: the combination of microgrids and renewable sources may be an answer to the increase of the energy cost, strongly related to the fuel cost. Moreover, it could be observed the potential of a microgrid to sell ancillary service to the distributors, and to actively participate at the electricity market, both as purchasers and as sellers. For a future significant implementation of microgrids in distribution network, it is very important to determine the economic benefits and to propose methods and tools able to

III. OPTIMIZATION OF THE PARTITION IN MICROGRIDS The optimal subdivision of a given distribution network with DG installed into microgrids may be stated as the individuation of every sustainable cluster of DG and load nodes (i.e. characterized by a reasonable probability that the DG feeds the microgrid in the autonomous operation) which can be grouped to form a microgrid. This reconfiguration of the network in microgrids may permit to achieve savings in terms of avoided cost of energy and interruptions. One of the objectives of this paper is to evaluate the amount of these savings during one year of network operation and to develop a procedure able to maximize them. A. The Objective Function The procedure presented in the paper maximizes the objective function Fobj given by the sum of the savings in the energy purchase, JE, and in the cost of expected energy not supplied, JEENS, associated to each feasible network configuration with some microgrids in the lateral feeders (1). (1) Maximize Fobj = ( J E + J EENS )

The energy net saving JE is given by (2), where Nm is the number of microgrids considered in the given network configuration. Nm

J E = ∑ (CEi − CMi )

(2)

i =1

CEi is the cost of energy paid by customers when they are not arranged in microgrids. CMi is the balance between the cost of energy paid by the same nodes and the revenue achieved by selling the DG energy surplus in the bulk energy market. JEENS is the difference between the cost of the energy not supplied considering or not considering the microgrids (3). Nm

J EENS = ∑ (CEENSi − CEENSMi )

(3)

i =1

CEENSi represents the cost of the energy not supplied with no active microgrids in the network and with intentional islanding not allowed. CEENSMi is the cost of energy not supplied calculated considering the opportunity that the generators in the microgrid provide the energy to the loads when the microgrid is isolated from the network due to outages (intentional islanding allowed). The cost of the energy not supplied is related to the costs that customers have to sustain to face interruptions. It may be assessed as a function of the duration of interruptions and the customer typology (residential, industrial, etc.) with (4) [7].

3 Ni

CEENSi = ∑ (kk ⋅ Pk )

(4)

k =1

Where Ni is the number of nodes that constitute the i-th microgrid, kk is a cost factor depending on the interruption duration and the customer typology respectively and Pk is the power drawn by customers. B. The proposed algorithm The maximum of the objective function is obtained with the analysis of every possible combination of sustainable microgrids in a MV network, ordered in a merit list. A Sequential Monte Carlo (SMC) algorithm on hourly basis has been implemented to consider the interaction between loads and generators (comparison of available power production and load demand), and the availability of generators, lines, and switching devices. In the paper the daily load curve of each load point has been discretized into 24 intervals and a probabilistic approach is adopted by assuming for each interval a normal distribution of probability. The same approach has been adopted for the generators, but the probability density function depends on the primary energy source used. Furthermore, the price model for the economic evaluation of the microgrids is also based on a Sequential Monte Carlo, due to the difficulties associated to the analytic forecasting method. The flowchart of the proposed algorithm is shown in Fig. 1. Once the network with existing DG units has been acquired, the algorithm proceeds with the three main steps as follows: 1) Generation of sustainable microgrids in lateral branches: individuation of the nodes (candidates) that could be the Microgrid Point of Coupling (MPC) for a sustainable microgrid; in lateral branches the power drawn by loads is compared with the available DG power. 2) Economic evaluation of microgrids: the paper mainly considers the customer’s point of view and it is focused on the savings that they can obtain by accepting to belong to a microgrid optimally constituted. For this reason a net saving JE is calculated and associated to every potential microgrid (individuated in Step 1). The JE is assessed by comparing the cost of energy to customers when they are directly fed by the network with the same cost when they can directly use the energy resources in the microgrid and sold energy surplus to the market. Furthermore, the two situations are also compared with reference to the service reliability (e.g. lateral nodes not in microgrids cannot be repowered and suffer for long duration interruptions). The cost of energy related to each microgrid is calculated with an hourly basis operated SMC. Such methodology permits to simulate the hourly prices of energy purchase according to Day Ahead Market of the Italian Power Exchange, under the hypothesis that, in case of a production surplus, the microgrid sells such surplus to the market. The statistic model for the determination of the energy market prices is based on the trend of Italian energy market during the period from April 2004 to March 2005. According to the Italian situation two

START Acquisition of existing network Generation of alternative grids in lateral branches Economic evaluation of microgrids Combination costruction Reliability evaluation Fobj calculation End combinations?

no

yes Optimal combination END

Fig. 1. Flowchart of the proposed algorithm

main prices have been used: the average single national price (purchase price) and the zonal sale price. In Fig. 2 the mean values assumed in the electricity market (year 2004) by the energy sale price in different parts of Italy are pointed out, as well as the gap between the sale and the purchase prices. Four hourly tariffs related to the load has been considered (full load, high load, medium load, empty hours), dictated by the authority as expression of the different value of the electric energy during the day. Then, also a subdivision by seasons has been done and the medium value of the purchase price for each load condition and for each season has been individuated and used by the SMC. Finally, by using all the data available the prices for the energy purchasing and selling may be individuated for each hour of the year. Such price signals are used by the Energy Management System (EMS), which decides the set point of all generators considering also the load demand and the generators availability. Table I summarizes the possible actions implemented in the EMS. As it can be easily seen the existing relations among the energy purchase/sale prices, the DG cost of production and the load demand lead to six potential situations. Let assume that CDG is the DG cost of production, PDG the hourly power production of generators in the microgrid and Pload the active power drawn by customers. Whether CDG is lower than the purchase energy price and generation is sufficient to feed customers in the considered hour, the EMS decides to feed the loads with DG (Scenarios 1 and 2 in Tab. I). Furthermore, if the generation capacity is not already completely exploited, the EMS, according to the energy sale price, can fix the DG set-point to sell energy to the

4 TABLE I SCENARIOS IN MANAGING A MICROGRID Scenario

CDG< Purchase Price

Sale Price > CDG

PDG > Pload

1 2 3 4 5 6

yes yes yes yes no no

yes no yes no yes no

yes yes no no ---

bulk system. This action is economically justified only if the selling price is greater than CDG. In some other hours, the DG capacity would be not sufficient to meet the load in the microgrid (Scenarios 3 and 4 in Tab. I). In this situation the EMS may perform two measures: it can decide to dispatch the DG in the microgrid at maximum output to feed loads (the energy deficit will be covered by the network as in the Scenario 4 of Tab. I) or it may decide to purchase the energy for the loads from the system and selling energy to bulk system (Scenario 5 in Tab. I). If selling the power to the grid is not economically convenient (Scenario 6 in Tab. I), it may be opportune to operate a DG generation curtailment to the minimum production. For each hour of the day the EMS chooses the most profitable set-point of generators on the basis of load demand, energy prices and DG availability and it is possible to assess the hour economic balance of the microgrid. The yearly balance is evaluated as the sum of the hourly balances. By considering all the microgrids in the network, the global cost of energy for those customers fed by microgrids (CM in Equation 2) is the sum of each evaluated yearly balance. The situation of no control in the microgrid (loads supplied by the network) is recalled each hour by the algorithm to be used in the determination of the objective function, as the comparison term of the estimation of the energy cost JE. Then, the mean saving related to each microgrid (1st term of the objective function) is obtained as the difference between the mean value of the year balance with no control of the

Fig. 2: Italy, 2004: Mean value of energy sale price and gap by the purchase price

Network management Loads fed by DG DG surplus sold to the grid Loads fed by DG DG surplus not produced Choose the best between 4 or 5 scenarios Loads fed by DG Power deficit bought by the grid Loads fed by the grid DG power sold to the grid Loads fed by the grid Minimum DG power produced

microgrid and the same term obtained in microgrid operation. 3) Reliability assessment: the reliability assessment is performed with the SMC that considers, besides the energy balance in the microgrid, also the reliability of lines, sectionalizers and generators. The most important benefit of a microgrid with reference to reliability is that it can be quickly separated by the network during faults and it can feed loads during the fault repair stage. This feature is really important for loads supplied by lateral branches that are normally subjected to a poor level of service continuity. Once the expected number and duration of interruptions for the customers supplied by a microgrid have been calculated, the cost of energy not supplied is assessed by considering interruption curves for residential, industrial, service, and commercial customers [7]. At the end of the iterative procedure, each microgrid is described by a cost, and the global cost corresponding to each microgrid combination can be calculated with (1) so that the solutions may be ordered in merit list. IV. CASE STUDY The proposed methodology has been applied to the portion of Italian 20 kV distribution network depicted in Fig. 3. It presents 2 HV/MV substations that supply 142 MV/LV nodes, divided in 25 trunk nodes and 117 lateral nodes. The network contains several DG units of different sizes, ranging from 200 to 1000 kW, positioned in the lateral feeders. The annual medium active power delivered to MV nodes is 6 MW. Considering the DG available, the number of microgrids which may be originated depends on load and generation curves. In fact, they play a fundamental role in determining whether an intentional island may be formed or not. For example, considering the lateral feeder highlighted in Fig. 3, several microgrids may be originated: three of them are depicted in Fig. 4. Different DG technologies have been considered in the tests. For the sake of brevity, only the results obtained by considering microturbines (MT) and wind turbines (WT) are reported. In case of gas-fired MT the production cost of the energy has been assumed equal to 0.065 €/kWh, in case of WT equal to 0.05 €/kWh, typical of medium/high wind speed areas [8], [9]. Obviously the main difference between the two sources is not only the cost of production but, above all, the possibility to be dispatched at request (MT) or not (WT). In order to assess cost of the energy not supplied, a fault rate of 0.15 failures/year·km and 0.10 failures/year·km have

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Legend

MPC 99

HV/MV substation MV trunk node MV lateral node MPC Microgrid Point of Coupling

MG2

DG site

MG1

MPC 35

MPC 28 MPC 126

Fig. 4. Possible microgrids for the lateral highlighted in Fig. 3

MPC 22

MPC 20

MPC 39

MPC 7

MPC 27 MPC 10

Fig. 3. Test network TABLE II INTERRUPTION COST FACTOR k [€/KW] Customer Residential

Duration of Interruptions - T [min] T≤20 0.027

20