Optimal Utilization of Distribution Networks for Energy ... - IEEE Xplore

IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 22, NO. 1, FEBRUARY 2007

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Optimal Utilization of Distribution Networks for Energy Harvesting Andrew Keane, Student Member, IEEE, and Mark O’Malley, Senior Member, IEEE

Abstract—The introduction of distributed generation (DG) is leading to a fundamental change in how distribution networks are utilized and viewed. Distribution networks are now used as a means to connect geographically dispersed energy sources to the electricity system, thereby converting what were originally energy delivery networks, to networks used both for the delivery and harvesting of energy. This paper presents a methodology which maximizes the amount of energy that may be reaped from a given area, while taking account of the available energy resources, connection costs, losses, frequency of constraint breaches, and other technical constraints. The optimal energy allocation is determined for a sample section of network, illustrating the implementation of the methodology and the scope for non firm access to the distribution network. Index Terms—Dispersed storage and generation, energy resources, linear programming, losses, power distribution planning.

I. INTRODUCTION

T

HE ROLE of distribution networks is changing. What were originally passive networks purely for the delivery of electricity to the consumer are now networks that are being utilized for the harvesting of energy from a myriad of distributed energy resources. The increased proliferation of these distributed generators has lead to changes in the characteristics of the network, with more variable and bidirectional active and reactive power flows. These generators are altering the technical characteristics of the networks and pushing them to operate closer to their limits of safe and reliable operation. A number of drivers have fuelled the interest of independent developers to consider investing in low capital, small scale, fast revenue-generating projects, such as wind and biomass generation [1]. In accordance with EU Directive 03/54/EC, all EU countries are in the process of opening up their electricity sector to competition [2], facilitating the introduction of distributed generation (DG). In addition, under the EU Directive 2001/77/EC, 12% of EU electricity generation should be from renewable sources by 2010 [3]. The vast majority of DG in Europe is from renewable sources. The combination of all these drivers has led to a large volume of applications for access to the distribution network. In particular, in Ireland at the end of Manuscript received April 3, 2006; revised August 8, 2006. This work was conducted in the Electricity Research Centre, University College Dublin, which is supported by ESB Networks, ESB Powergen, ESB National Grid, Cylon, Viridian, the Commission for Energy Regulation, and Airtricity. The work of A. Keane was supported by Sustainable Energy Ireland under a postgraduate research scholarship from the Irish Research Council for Science Engineering and Technology. Paper no. TPWRS-00187-2006. The authors are with the School of Electrical, Electronic, and Mechanical Engineering, University College Dublin, Dublin 4, Ireland (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TPWRS.2006.888959

2004, applications received by the system operators concerned the connection of approximately 2500 MW of wind generation. A significant amount of this capacity is to be distribution connected. This is in addition to approximately 500 MW of previously contracted wind farm capacity and approximately 100 MW of other forms of DG such as landfill gas (LFG) and hydro. In a country with a peak load of approximately 4800 MW, this is an extremely large amount of DG to integrate into the distribution network [4]. The priority given to renewable sources under EU Directive 03/54/EC along with the worldwide promotion of renewables presents a challenge to network operators in that they must, to some extent, accept the connection renewable generators and give them priority dispatch, subject to the technical constraints. However, there is still scope for optimization of the planning and operation of DG as demonstrated by previous publications. These publications have addressed various impacts of DG on the distribution network, addressing the optimal placement of DG to maximize capacity with particular regard to various constraints. In [5], a multiobjective planning strategy is presented using a genetic algorithm to identify the best compromise DG sizing and siting. Other work has focused on the reliability worth of DG [6] and the consideration of an optimal operating strategy for DG on an hourly basis. In [7], a probabilistic reliability model is presented to determine the impact of DG for use in distribution planning studies. Work has also been done evaluating the contribution of wind generation, in particular, to reliability [8], [9]. The issues of load growth and load patterns in distribution planning are discussed in [10], and a multistage approach to planning is described in [11]. In [12], the amount of losses incurred with increasing penetrations of various DG sources is examined. The placement of generation on a first-come first-served basis invariably limits the overall capacity of DG, through network sterilization as shown in [13] and [14]. Network sterilization results when capacity is allocated to the bus/buses that are most sensitive to power injections, with respect to the technical constraints, thus limiting the amount of further generation that can be connected at the other buses. In [15], a method is presented using optimal power flow for the allocation of generation capacity, which includes a detailed fault level constraint. In [16], the authors developed a methodology to optimally allocate DG capacity on the distribution network. The constraints considered were voltage rise, thermal limit, short circuit capacity, short circuit level, energy resource, and customer initiatives. The methodology ensured that network sterilization was avoided and the network capacity maximized. These papers all assess the amount of firm DG capacity that may be connected. The amount of firm access granted under the connection agreement to a distributed generator is the level of output at which

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they can always operate without violating any of the constraints on the network. Non firm access refers to output greater than this amount, at which generators may be allowed to operate dependent on the load and generation levels throughout the year. This paper seeks to take account of the issues of temporal variations, connection costs, losses, technical constraints, and also to assess the potential for non firm access, when utilizing distribution networks for energy harvesting. The available energy resources impact the energy that can be garnered from the allocated capacity. Traditionally, DG capacity has been allocated on a “worst-case scenario” basis. For example, the voltage constraint is evaluated for the peaking condition of maximum generation, minimum load. This paper assesses the scope for non firm energy beyond these strict limits and optimizes the allocation of this non firm energy, such that the constraint breaches are reduced and hence the energy harvested is maximized. This methodology maximizes the energy delivered from DG and shows that extra energy can be reaped through non firm access. Section II describes the various factors that should be considered when analyzing the impact of DG on distribution networks. Section III describes the formulation of the methodology, including the objective function and constraints. Section IV gives the characteristics of the test system used. Results and discussion are given for a sample section of distribution network in Section V, illustrating maximization of DG energy and the potential for non firm access. Comparisons between different cases are given. Conclusions are given in Section VI. II. ENERGY HARVESTING DISTRIBUTION NETWORKS The optimal integration of DG onto what are now becoming energy harvesting networks is dependent on a number of factors. These factors are described here.

TABLE I GENERATION LOAD FACTORS

will avoid the construction of long lines, to facilitate a relatively small amount of energy. The minimization of connection costs is effectively the minimization of conductor length, which has the added benefit of reducing the environmental impact of connecting DG, along with the connection losses incurred. C. Losses A certain amount of electrical losses due to the flow of power is inevitable, and as such, these losses create an operating cost [10]. Similar to any operating cost, it must be balanced against other costs and objectives. Distribution loss adjustment factors are used by many network operators to take account of the average impact of DG on losses resulting from distributed generators [17]. These loss adjustment factors are applied to the energy metered at the point of connection to the network. A method has previously been developed in [18] to calculate individual loss factors for each bus, taking into account the amount and type of generation connected at each bus. The loss factors reward generators for ameliorating losses and penalize them for increasing losses on a site-specific and energy resource-specific basis. By their nature, losses vary nonlinearly with changing power flows, nonetheless by utilizing the available knowledge of load behavior, energy resource load factors, and network characteristics, the average effect of DG plant on losses may be determined.

A. Capacity The optimal allocation of capacity with regard to technical constraints has previously been determined in [16]. The optimization is determined for a peak condition (maximum generation and minimum load). It has been shown previously in [13] that network sterilization can be severe and is avoided by the maximization of capacity. B. Siting Restrictions A specific, finite, and geographically located energy resource is considered. This implicitly addresses the issue of siting restrictions. In particular, the connection costs can be a significant cost for the generator. While the capital costs will vary per MW of installed capacity, the connection costs are largely independent of the capacity, given the discrete nature of available line ratings, and are a function of the length of the line, rather than the amount of energy that flows down it. Excessive connection costs may present a barrier to the connection of the generator and are explicitly dependent on where the generator connects. The capital and operation costs of each generator are not included as they are not viewed as a barrier that would prevent the connection of a generator, i.e., they are independent of where the generator connects and are not within the control of the network operator. Hence, the inclusion of connection costs

D. DG Load Factors Load factors (LFs) express the energy output of a generator as a fraction of the maximum possible energy output that is produced by a generator in a year. The inclusion of load factors in an optimization effectively converts the capacity (MW) at each bus to energy output (MWh) at each bus, i.e., a 5-MW biomass plant will supply more energy than a wind farm of the same size, over a year based on their LFs. In this manner, the allocation of available capacity is now done, with regard to the available energy output of those resources. LFs only represent the average output of a generator, and as such, the actual output profile of each energy resource cannot be taken account of by them. Generic LFs for various energy sources are well established and are shown in Table I [9]. It can be seen that there is a diverse range of values for the various generation technologies. The specific value of the load factors can vary depending on the energy resource and plant operation. The fraction of this energy that is actually delivered to load or exported to the transmission system is dependent on the losses incurred. The loss adjustment factors determined are used to calfor each energy resource culate an effective load factor and bus. In each case, the LF is scaled slightly upward or downward depending on the impact on losses.

KEANE AND O’MALLEY: OPTIMAL UTILIZATION OF DISTRIBUTION NETWORKS FOR ENERGY HARVESTING

E. Firm and Non Firm Access Non firm access to the distribution network may facilitate the connection of more DG in an economical way, especially when compared to firm reinforcement options. The voltage constraint has been demonstrated to largely be the dominant constraint and hence limit to further DG capacity [13], [20]. It has tradipeak condition. tionally been assessed at an infrequent While infrequent, it is important for the operation of the system that the voltage stays within its limits. Existing generators of all types have the ability to ramp down their output if required to do so. In addition, a number of innovative voltage control techniques have been proposed to get around the voltage rise effect [21]–[24], along with solutions to the other technical constraints such as the short circuit level [25]. Although, in more urban areas where the short circuit level may be more significant, active management of fault levels is some way off and is likely to be very expensive [26]. The active control methods mentioned above can overcome the voltage constraint. However, frequent constraint breaches are still not favorable, as they will result in either excessive amounts of energy being curtailed by the generators or excessive wear and tear on the transformer tap changer for voltage control. A methodology is given here that optimizes the non firm energy allocation on a given section of distribution network. The methodology maximizes the energy from DG per euro of connection cost, while also minimizing the voltage rise and losses on the network. The minimization of the voltage rise is equivalent to minimization of the constraint breaches and thus equates to the maximization of non firm energy. III. METHODOLOGY The objective of the methodology is to maximize the amount of DG energy harvested per euro of investment by making best use of the existing network assets and available energy resource. This is done subject to the technical constraints on the network. The optimization is carried out from the point of view of the network operator and their responsibility to utilize the available capacity optimally and to facilitate increasing penetrations of DG, while maintaining the standard of supply to electricity consumers. The rationale behind the objective function in (1) is that network operators are now dealing with increasing applications for DG from various energy sources and that the available capacity is a valuable asset and should be optimally utilized. Hence, the optimal allocation of the available capacity is viewed as that allocation, which maximizes the amount of energy reaped from the available energy resources and network capacity while minimizing the connection costs of the generators. Network costs are not included because the aim is to maximize the amount of energy that can be reaped without incurring any further network costs. A discussion of network costs and possible planned reinforcements is given in Section V. The additional factor of non firm access is taken account of with the inclusion of bus voltage sensitivities in the objective function (1), which are employed to reduce the instances and level of curtailment. The optimization problem is formulated as a linear program (LP), with an iterative procedure employed to take account of the amount of constraint breaches that arise with non firm access. The inclusion of the

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ELFs means that the plant is allocated based on the amount of energy that is delivered. The objective function J(MWh/ C kV) given in (1) is maximized as follows: (1) is the th energy resource. are the conwhere trol variables representing the fraction of allocated to . the th bus on the th iteration, i.e., and are the number of energy resources and buses, respecis the connection costs of the th energy retively. source at the th bus. The connection costs are variable per km gives the effective load factor of the th energy of line. th iteration. (kV/MW) resource at the th bus on the gives the total voltage sensitivity of the th bus to power injections at all other buses on the th iteration. The voltage sensitivity is included in the objective function, such that each bus is weighted according to its voltage sensitivity. This has the effect of minimizing the voltage rise, which leads to reduced constraint breaches. The calculation of these bus voltage sensitivities and effective load factors is shown below. The voltage constraint has been previously employed to ensure that voltage levels are kept within the specified limits. From [16], it is given by (2) where refers to the dependency of the voltage level at bus on power injections at bus . refers to the initial voltage level at the th bus with no generation, is the amount of DG connected at the th bus, and is the number of buses. is the maximum permissible voltage. From (2), the total voltage sensitivity of each bus to generation on the th iteration is given by in the following: (3) in (3) are those relating to the feeding The values for condition, i.e., the normal forward feeding condition. These values are used as this is the most frequent feeding condition throughout the year and therefore most accurately represent the voltage sensitivity of each bus. The loss adjustment factor for the th bus and th energy reis given by [18] source on the th iteration (4) where and are the base amount and generation amount of losses related to the th bus, respectively. is the allocation of the th energy resource to the th bus on the th iteration. is determined by calculating the losses when there is no generation connected at the th bus. By utilizing the allocation of the th energy resources to the th bus on the iteration, is calculated. The updated are fed into (5), which shows the calculation of the ELFs as follows:

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characteristics of the network. The constraints of line and transformer ratings are given by (6) and (9), respectively. Equation (7) gives the short circuit level constraint, and (8) gives the short circuit ratio constraint. This constraint is a used by network operators to ensure that the voltage dip experienced when the DG is lost is kept within acceptable limits [27], [28]. As can be seen in the Appendix, all the constraints are determined with respect . They are determined indeto the maximum capacity pendently of the individual energy resources and their respective load factors. IV. TEST SYSTEM A. System Data The test system chosen is a typical section of the Irish 38-kV distribution network. Results are given here for a seven-bus section as shown in Fig. 2. The section of distribution network is modeled in DIgSILENT Powerfactory. Load flow and short circuit analysis is used to determine and formulate the various constraints and factors used in the optimization. Load values for each bus were obtained from ESB National Grid [29] and ESB Networks [30]. Annual simulations are carried out to assess the optimality of the allocation as determined from the objective function given in (1). They are also used for comparison of different allocations. These simulations consist of load flow calculations carried out for half hourly data. The data used in the simulations include actual historical active and reactive profiles for each of the energy resources and loads, along with data on outages and the sending voltage at the transfrequency of mission station. These data were also obtained from ESB National Grid and ESB Networks. The load factors used in the objective function for each energy resource match the load factors of the real generation profiles used in the annual simulation. B. Voltage Sensitivity Fig. 1. Iterative method.

(5) An iterative technique is used for the voltage sensitivities and effective load factors, which updates the values of and based on the values of . The values determined for are used to calculate the values for . is the product of and for each energy resource at the th bus. Initially, equal amounts of generation at each bus are assumed (1 MW). Buses at which the initial allocation is zero are set to zero for subsequent iterations. The values of and are compared after each iteration, and the methodology terminates when they converge to within a tolerable level (0.0001 MW). The flowchart in Fig. 1 illustrates the implementation of this iterative technique. The objective function in (1) is maximized with respect to the technical constraints in (6)–(9), given in the Appendix [16]. These constraints limit the DG allocation based on the technical

The sensitivity of the bus voltages to increasing generation is determined. The sensitivity , from (3), takes into account both the sensitivity of the voltage to power injections at the th bus and all other buses. It is evident from the network topology in Fig. 2 that the voltage at some buses will be independent of generation at other buses. Table II shows the voltage sensitivity of each bus to power injections at all the buses under normal feeding conditions, rounded to four decimal places. As mentioned in Section III, this value is used as it is the most common feeding condition. The sensitivities, which are not shown here, were also calculated for use in the voltage constraint of (2) in the firm allocation. The bus voltage sensitivities are dependent on the amount of impedance between each bus and the transmission station and on the load levels at that bus and every bus between it and the transmission station. It can be seen from the voltage sensitivities in Table II that buses C, D, and F are most sensitive to power injections, which is to be expected given their distance out from the transmission station. The interdependence between bus B and buses C and D is, perhaps unexpectedly, zero when rounded to four decimal places; this is because it is closely linked to the voltage at the transmission station by a 1-km line. Buses C and D are quite strongly interdependent. It can be seen that the addition


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Fig. 2. The 38-kV seven-bus radial distribution network diagram.

TABLE II BUS VOLTAGE SENSITIVITIES

(KV/MW)

maximum change from the base value of load factor is approximately 3%. The values used in the optimization were updated at each iteration based on the previous solution, as described earlier in Section III. D. Available Energy Resources

TABLE III EFFECTIVE LOAD FACTORS

of 1 MW at bus C increases the voltage there by 0.154 kV and also increases the voltage at bus D by 0.1258 kV. C. Effective Load Factors for each energy resource The effective load factors at each bus are given in Table III as calculated from (5) using the LAFs calculated from (4) [18]. The values shown in Table III are values calculated for 5 MW of each type of generation at each bus. They are included to show the different impacts of each energy resource at each bus. The ELFs now credit the generators with extra energy output (i.e., ) and debit their energy output for increased losses (i.e., ). The base values of the load factor are shown in Table I. It can seen that the

The optimal energy allocation is determined for a representative energy resource portfolio given in Table IV. The total energy resource amounts to 49 MW. The connection costs are vari/km. In this able per km of line and are taken to be case, an abundant energy resource is assumed, with a number of wind and hydro sites along with the potential for an LFG and a biomass plant. Table IV also shows the distances from each energy resource to each bus. While it is not practical to assume that all generators can connect to all buses, the distances are included and the optimization approach will favor shorter, inexpensive connection lines. In addition, the distances shown, in as much as is possible, attempt to take account of the likely line routes that would be chosen by the network operator. It may be possible for some generators connected to the same bus to share common line paths and share costs. In such a case, the line costs for the relevant generators would be reduced appropriately, with the cost of the shared line split between them. This situation does not arise with the area studied here but could be included if such a case arose. V. RESULTS AND DISCUSSION A. Energy Allocations The optimal energy allocation is determined using the methodology described in Section III and is shown in Table V. The values shown are the products of and , which give the amount of each energy resource to install at the th bus. The total generation plant allocated is 41 MW. This

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TABLE IV ENERGY RESOURCES (MW) AND LINE ROUTES TO BUSES (KM)

TABLE VI BUS ALLOCATIONS PDG (MW)

TABLE VII ENERGY OUTPUT (MWH)

TABLE V OPTIMAL ENERGY ALLOCATION (MW)

allocation maximizes the energy delivered from the available energy resources per euro of connection costs and per kV of voltage rise. The allocation converged to a solution on the fourth iteration . The energy allocation hits the constraint of the transformer rating given in (9). Evidence of the maximization of energy objective can be seen, as the energy resources with the highest LFs are fully allocated, while it is the Wind 3, Wind 1, and Hydro 1 allocations with relatively low LFs that are not fully allocated. To assess the impact of losses on the solution, the optimization was repeated without the inclusion of the loss adjustment factors, and the same solution allocation was arrived at after the same number of iterations. The conclusion was drawn that for this case, the factors of connection costs and voltage sensitivity are dominant and that the DG losses do not impact the solution determined. The optimality of this non firm allocation from (1) ( kV) is now demonstrated through comparison with two other possible approaches. First, it is compared with the base case of the maximum firm allocation, which is optimized on the basis of . This approach includes the voltage constraint given in (9). Second, it is compared to another non firm case, where neither the voltage rise sensitivity nor the voltage constraint in (2) are included. This objective function is optimized with respect to but takes no account of constraint breaches, effectively ignoring the voltage levels. Therefore, this objective function does not reduce the amount of energy lost through curtailment. Table VI shows the different bus allocations for the various approaches. It can be seen that between each of the three approaches, allocations to each bus vary widely. This indicates that a clear objective at the start of the DG planning process

is preferable and will lead to a more optimal solution. It can be seen that on a capacity basis alone, the DG capacity is effectively doubled to 41 MW from 19.7 MW, when non firm access is permitted. Of particular interest is the difference between the two non firm allocations. The total capacity allocated amounts to 41 MW in both cases, with the transformer constraint in (9) being the binding constraint. However, it can be seen that the inclusion of voltage rise in the objective function largely results in the allocation of generation away from the buses with the highest sensitivity to voltage. Buses C and D have much reduced allocations when voltage is included in the objective function. The objective function includes a number of factors, and so, the objective of voltage minimization is balanced against the need to minimize the connection costs. B. Annual Simulations Each of the three allocations shown in Table VI were simulated over a year to assess their respective levels of energy output and also the occurrence of constraint breaches. The annual simulations are used to test the effectiveness of the energy harvesting methodology. Table VII shows the energy output of the three cases. The optimal firm allocation of 19.7 MW translates to an annual energy output of 114 217 MWh. With non firm access, there is an increased potential energy output to 188 798 MWh, an increase of 65.3%. The doubling in capacity does not lead to an equivalent doubling in energy, as the extra capacity is made up of energy resources with relatively low LFs. The results demonstrate that, where there is a surplus energy resource available, there is scope for the accommodation of this energy through non firm access. Firm access is based on strict deterministic limits and allows the distribution system to continue to operate passively, of which there are undoubted benefits. However, as has been shown here, if these limits are relaxed, it opens up scope for the further capture of energy. The potential for non firm access has been demonstrated, but the question arises of the optimization of the non firm allocation. The objective function shown in (1) weights the buses according to their voltage sensitivity and losses, thus maximizing the non firm energy that may be captured. The two non firm allocations shown above illustrate the scope for this approach. The same amount of capacity is installed in both cases as shown in Table VI; however, when the voltage rise is ignored, generation is allocated to the buses that are more sensitive to power injections. The result is an allocation that has the same amount


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TABLE VIII VOLTAGE QUALITY

of capacity but is curtailed for a much larger percentage of the time. In this case, 13 680 MWh is curtailed, which equates to 18.34% of the potential non firm energy. In contrast, when the voltage is taken account of in the objective function, only 902 MWh or 1.21% of the potential non firm energy is curtailed, as is shown in Table VII. This demonstrates that for the case shown, the use of the objective function in (1) reduces the instances and amount of energy curtailed over a given year. C. Power Quality The focus of this paper is on voltage rise and reducing the magnitude and instances of overvoltage conditions. However, an indirect benefit of using the voltage sensitivities to optimize non firm access is the impact of the allocation on power quality. By allocating to the buses least sensitive to voltage, the average voltage variance across the network should decrease. The maximum permissible voltage is taken to be 41.7 kV on the 38-kV network. Table III shows the average voltage variance, maximum, and minimum voltages seen on each bus over the year simulated for the three cases considered. and refer to the minimum and maximum voltages measured over the year, respectively. It can be seen from Table VIII that the inclusion of voltage sensitivities in the objective function does in general improve the voltage quality, as measured by variance, across the buses. However, it is evident from the voltage variance at buses B and F that a more variable voltage results when voltage sensitivities are employed. This highlights the impact of the energy resource, and its corresponding output profile, on the voltage variations. The values for show that when the voltage sensitivities are employed, the severe cases of overvoltage can be reduced, with the maximum voltages of 41.81 kV and 42.57 kV at buses C and D, being reduced to 41.28 kV and 40.82 kV, respectively. D. Allocation Costs The results shown in Table IX show how the energy allocations in Table VII translate to costs. The annualized production cost (APC) in C /MWh for each non firm case is shown. A project lifetime of 20 years and an interest rate of 7.5% was assumed for this calculation. The capital costs of some energy resources are better established than others. However, values are available per MW installed for all major types of distributed

TABLE IX ALLOCATION COSTS ( C )

generation [31]. The capital costs of the plant are taken to be fixed regardless of where each energy resource is connected. The costs for the firm allocation are shown in Table IX. It can be seen that the 19.7 MW of firm capacity can be connected significantly cheaper than the 41 MW of non firm capacity. The two non firm allocations have identical capital costs. However, the consideration of the voltage rise results in increased connection costs of C 450 000. However, the benefits of increased energy export outweigh the increased costs, as demonstrated by the APC for both allocations shown in Table IX, where the optimal non firm allocation ( C kV) has a 6% lower APC of 28.30 C /MWh. The results show that the connection costs are a significant factor to be considered when allocating energy resources. The connection cost is offset by the minimization of voltage, as can be seen from Table IX, where the minimization of voltage leads to higher connections costs, but facilitates the export of more energy and hence a lower annualized production cost. To put the costs into perspective, the cost of firm reinforcement of the network to accommodate 41 MW of capacity was determined. The reinforcements are the uprating of lines. The level of reinforcement was determined by uprating relevant lines and running the firm optimization until 41 MW could be accommodated. The lines were uprated from the existing 100-SCA (steel core aluminium) lines to 300-SCA lines, which are the standard conductor types used at this voltage level by the network operator in Ireland [30]. The cost of the 300-SCA line is taken to be 62 000 C /km. It was found that to accommodate 41 MW of firm capacity would require the uprating of 97 km of line to the next standard conductor size at a cost of approximately C 6 000 000. This is shown in Table IX with the C 6 000 000 being added to the connection costs. Firm reinforcement would remove curtailment but, as has been shown above, if optimized, the amount of curtailed energy can be kept very low, avoiding the necessity of expensive reinforcement. This expensive reinforcement

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causes the firm reinforcement option to have the highest APC of 31.05 C /MWh. In addition, the firm reinforcement of the network is likely to take much longer to implement, delaying the connection of DG. As a means to demonstrate the potential benefits of non firm access over firm access, network costs were not included in the optimization, but rather, the network is taken as fixed and the available capacity used optimally, which allows a clear comparison between the firm and non firm cases. However, the network operator may be planning to upgrade the network to accommodate increased and/or new loads, which will impact the solution determined. In such a case, the amount of firm access at certain buses would increase, possibly eliminating or reducing any curtailment at those buses. This could be taken account of in the calculation of the APC by recalculating the amount of energy produced over the project lifetime with the increased firm energy included. In terms of the optimization, if a new line is to be built or an existing one upgraded, the planned network could be used in the optimization to determine the optimal allocation. In such a case, the allocation would be suboptimal until the upgrading work is carried out; however, over the project lifetime, it may work out to be more optimal. Even if no network expansion is required, the load is still likely to grow over the DG project lifetime. In terms of voltage rise, increased load will only serve to ameliorate this problem and in turn reduce the amount of curtailment. The APC calculations could be modified to take account of this by recalculating the curtailed energy for each year with the increased load. This would potentially narrow the benefit of non firm access over firm access. Load growth and patterns and, as a result, network costs could potentially be included in the optimization, by employing a multistage approach to DG planning akin to the methodologies described in [10] and [11]. System reliability is also affected by load growth and changing load patterns and indeed by the connection of DG. In a similar fashion, and by using the load factors of the loads themselves, reliability could potentially be taken account of in the methodology.

optimization of the plant operation, if a dispatchable plant such as biomass is available. In addition, the output of wind generators is variable and relatively unpredictable; as such, this will affect the number of constraint breaches. However, this cannot be accurately represented at the planning stage and can only be represented as an average value by the load factor as described earlier. This highlights the scope for the optimization of the operation of DG sources as demonstrated in [6] and [12], which is beyond the planning issues discussed here.

E. Active Control and Dispatch

Short Circuit Level

It has been assumed in the results that the voltage constraint will be overcome by curtailment of generation. However, as mentioned previously, a number of voltage controllers have been proposed, which would eliminate the need for curtailment. These voltage controllers are a viable alternative to curtailment, but it has been shown here that if the voltage rise is considered, the amount of curtailed energy can be reduced considerably. If a voltage control scheme is preferred by the generator or network operator, the reduction of voltage rise still has benefits. The reduced constraint breaches will mean that far less control actions (i.e., tap changes) are required, reducing wear and tear on the network equipment. The operation of each plant is not optimized. The level of correlation between generation profiles will impact the amount of non firm energy harvested. The profiles used in the annual simulations have not been altered; as such, there may be scope for

VI. CONCLUSION Distribution networks must now balance their original responsibility of delivery of energy to consumers, with their new role of harvesting energy from dispersed energy resources. An approach has been proposed, which enables the optimal allocation of DG energy, which maximizes the DG energy per euro of connection costs. The approach here takes account of all relevant technical constraints, siting restrictions, temporal issues, losses, and connection costs. Significant scope for non firm access to the distribution systems has been shown, with an increase in energy capture possible. In addition, optimization of the non firm allocation leads to reduced constraint breaches and hence more energy export. Non firm access has been shown to be a viable alternative to expensive firm reinforcement of the network. It has also been shown that there should be a clear objective at the outset of the DG planning process, as the results vary widely between different approaches. APPENDIX Thermal Constraint

(6) where is the current flowing from generator to bus is the maximum rated current for the line between each generator and its corresponding bus.

(7) is the dependency of the SCL at the transmission where station to power injections at bus . is the initial SCL at the transmission bus with no generation present, and is the maximum permissible short circuit level as laid down in the distribution code. Short Circuit Ratio

(8)


where

is the power factor at the generator.

Transformer Rating

(9) where

refers to the rating of the transformer, and is the minimum load level at the th bus.

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Andrew Keane (S’04) received the B.E. degree in electrical engineering from University College Dublin, Dublin, Ireland, in 2003. He is pursuing the Ph.D. degree in the Electricity Research Centre, University College Dublin. His research interests are in power systems planning, distributed generation, and distribution networks.

Mark O’Malley (S’86–M’87–SM’96) received B.E. and Ph.D. degrees from University College Dublin, Dublin, Ireland, in 1983 and 1987, respectively. He is currently a Professor of electrical engineering at University College Dublin and the Director of the Electricity Research Centre. His research interests are in power systems, control theory, and biomedical engineering.