Distributed Storage Capacity Reservations in Microgrid for LV Network Operation

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1 Available online at ScienceDirect Energy Procedia 103 (2016 ) Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid, April 2016, Maldives Distributed Storage Capacity Reservations in Microgrid for LV Network Operation Abstract Zhimin Wang 1, Yan Zhang 2, Bo Li 1, Ran Li 3, Zhipeng Zhang 3,* 1: China State Grid Jibei Electric Economic Research Institute, Beijing , China 2: China State Grid State Power Economic Research Institute, Beijing , China 3: University of Bath, Bath BA2 7AY, United Kingdom This paper introduces a novel method for Low Voltage (LV) network management with diverse distributed energy storage batteries in microgrid. These batteries are used for demand response, allowing distributed network operators (DNOs) to reserve part of storage capacity for relieving network congestion by releasing stored energy. The major difficulty in carrying out storage capacity reservations among diverse customers is the reserved capacity quantification for each type of customers based on the identified network pressure degree and duration. To overcome this difficulty, the algorithm of storage capacity reservation can be considered from both technical and financial sides. In the proposed approach, the amounts of reserved storage capacities are basically evaluated following two criteria: contribution of customers to network pressure and operational cost of storage battery. Besides, an optimal solution for allocating reservation amount is put forward through an enhanced mixed reservation algorithm. A case study is carried out in a practical network in UK to implement the proposed method for network pressure mitigation and investment deferral. Keywords:Network operation, energy storage, demand response, distributed energy management 1. Introduction In microgrid systems, energy storages are essential to take advantage of distributed generations. However, storage batteries are generally installed with high investment. In order to make the most of the batteries, a novel method is proposed to employ them for demand response, accommodating load increment from existing and potential customers, relieving network congestion and deferring network investment. A number of previous studies have been carried out to investigate responses energy price variations from distributed storages [1, 2]. * Corresponding author. address: zz291@bath.ac.uk Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, REM2016: Renewable Energy Integration with Mini/Microgrid. doi: /j.egypro

2 154 Zhimin Wang et al. / Energy Procedia 103 ( 2016 ) However, only a few studies [3, 4] took account of the impact of storage on distribution networks. Even though [5, 6] proposed approaches to utilize in-home storage to respond to energy price and network condition simultaneously, they paid more attention to the concept of multi-objective response rather than proposing a time-based storage dispatch scheme between customers and distributed network operators (DNOs) within a settlement day for effective network management. Besides, these investigations are carried out in distribution networks where identical customers and storage batteries are chosen for demonstration. Therefore, the diversities of end users and storage batteries were lack of consideration. In practical situation, different types of customers with diverse storage batteries are generally connected to a specific LV distribution system. This paper attaches importance to the investigation of exploring how to use diverse batteries at household level effectively for relieving network congestions. For a specific type of customers under distribution system, it is assumed that their load profiles and distributed storage capacities are identical. In this study, the proposed approach aims share the energy storage between customers and (DNOs) to respond to energy price and network pressure for energy cost saving and network investment deferral. Based on the identified network pressure in terms of overloading or overvoltage, a certain amount of storage capacity in each dwelling is able to be reserved by DNOs for network operation. The rest battery capacities are operated by customers to take the benefit of low energy price. For diverse customers in a distribution system, the storage capacities that are reserved by DNOs can be determined following the impacts of their load on network pressure from technical perspective together with the operational cost of their storage device from economic side. Compared with previous work, this paper has the following three key contributions: i) propose an approach for quantifying the reserved capacities of diverse distributed batteries in network congestion relief based on their contributions to the pressure; ii) investigate another method to determine the reservation amounts of different batteries according to the storage operational costs for network pressure mitigation; iii) put forward an improved storage reservation approach to take the advantages of the two storage capacity reservation algorithms mentioned in i) and ii). The reminder of the paper is organised as follows: The approaches to determine the reserved storage capacities, which are applied to different dwellings, for network management are described in Section 2. Section 3 provides a test system to demonstrate the proposed method. Finally, some conclusions are drawn in Section Methods of Storage Capacity Reservations Since network congestions are generally caused by either thermal limit violation or voltage limit violation, it is essential to determine how network pressure is driven by the two factors [7]. Once network congestion is identified, distributed storage batteries need to release part of their stored energy for network pressure mitigation. This part of energy can be reserved by DNOs instead of being used to respond to energy price. The detailed approaches for storage capacity reservation determination are proposed under thermal violation of circuit, voltage violation of circuit and thermal violation of transformer. This section tends to concentrate in the quantification processes of the reserved capacity determinations for each type of customers by the following three algorithms Pressure Contribution based reservation algorithm The first approach to determinine storage capacities reserved by DNOs is based on individual customers contributions to network pressure. Therefore, the contribution specifications are conducted before reserved capacities evaluations. Since different types of customers give various contributions to the identified congestion, the overloading or overvoltage condition is expected to be relieved by individual customers depending on

3 Zhimin Wang et al. / Energy Procedia 103 ( 2016 ) their own contributions to network pressure. which is defined as the contribution factor of peak demand for each customer under the i th type can be described by 1 Where d sy,t represents the peak demand along a feeder or at a substation. d h,i is the demand level of each individual customer categorised in the i th type at system peak time. Therefore, for each individual customer categorised in the i th type, the required reservation of its storage capacity is 2 L o and T in stands for the overloading level and duration caused by thermal/voltage limit violation respectively Economic reservation algorithm The storage capacity reservation algorithm developed based on customers contributions to network pressure totally follows power flow analysis from technical side. However, it cannot be ignored that DNOs are always willing to mitigate network pressure by the cheapest storage capacity reservation from the perspective of economics. Therefore, they prefer to dispatch the network pressure to the storage devices which have the least operational cost. The objective of the reservation with minimum spending is expressed as: Minimize e.g. Minimize C x, N )... C ( x, N )} (3) i ( i, e i n n, e n Where C re is the storage reservation cost paid by DNOs. C i stands for the function of storage operational cost calculation in the i th type. x i,e and N i are defined as the reserved capacity for a storage unit and the number of the i th type of customers contributing to network pressure respectively. The minimization of the objective function is subject to the following constraints (4) (5) E i is the total capacity of the storage battery owned by the i th type of customers Mixed reservation algorithm The pressure contribution based reservation algorithm is proposed from technical respective, following the principle that the capacity reserved by DNOs is proportional to its contribution to peak load. While, the economic reservation is conducted from financial side and it prefer to reserve the cheapest distributed storage. Theoretically, it is the most economical approach for DNOs to operate network, but it may not appropriate to encourage customers participation to DSR as its implementation may force cheap batteries to offer their capacity for network management and leave little capacity to respond to energy price. Therefore, in order to take advantage of both reservation algorithms, a mixed reservation algorithm is proposed. After network pressure identification and contribution specification of each type of customers, the mixed reservation algorithm can be justified that if it is pressure contribution driven or financial driven. The driven factor can be determined by the following process illustrated in Fig.1.

4 156 Zhimin Wang et al. / Energy Procedia 103 ( 2016 ) start Network congestion in system? No Yes Contributing households identification Pressure contribution driven in stead of financial driven? Yes Storage reservation based on contribution to network pressure No Storage reservation with minimising reservation cost subjected to the constraints of storage capacity limitation Rest storage respond to energy price End Fig. 1. The configuration of mixed reservation algorithm The quantification of storage capacity reservation fee paid by DNOs will be conducted in future work due to its complexity in calculation process. The main purpose of proposing the mixed reservation algorithm is to define two choices under different scenarios for an optimal reservation strategy. 3. Case Study In order to test the proposed methodology in a practical system, a radial LV network in UK are chosen for case study, given in Fig. 2 [8]. Fig.2. Layout of a radial LV network in Illminster Avenue The parameters of the test network, including feeder lengths and transformer capacities, are given in factors are chosen as 0.95 and 0.8 respectively [9]. The penetration level of distributed PV and storage is supposed to be 100%. In order to reflect the diversities of customers in the test system, typical domestic unrestricted customers are assumed to be connected with Feeder For Feeder 0012 and 0021, generic domestic Economy 7 [10] customers and non-domestic unrestricted customers are located. These types of customers are defined as Class 1, Class 2 and Class 3 of consumers respectively in demonstration. Their corresponding unit load profiles [11] are shown in Fig.3.

5 Zhimin Wang et al. / Energy Procedia 103 ( 2016 ) Load Fig. 3. Individual load profiles for three classes of customers In the whole test system, the price for the storage unit owned by a customer in Class 2 is artificially assumed the lowest. By contrast, the most expensive storage unit is installed for a customer belonging to Class 1. The operational cost of battery is proportional to the price of storage unit and the reserved capacity. For the storage units that installed for customers in Class 1-3, their storage capacities are set 4.8kWh, 4.8kWh and 6kWh respectively. At present, there is no network congestion in the test system after conducting power flow analysis. However, if load growth rate is selected as 2% per year, it can be observed that thermal limit violation occurs in Feeder 0011 in three years. The customers contributing to this pressure are identified as end users along Feeder 0011 and With employing storage capacity reservation approaches in terms of pressure contribution based reservation algorithm and economic reservation algorithm proposed in Section 2.1 and 2.2, the required storage capacity reservation amount and duration in three year for each individual customer connected with the whole system are shown in Table 1. Table 1. Storage capacity reservation amount and duration for individual customers in three years Class 1 Class 2 Class 3 Pressure contribution reservation (kwh) NA Economical reservation (kwh) NA Duration 17:30 19:00 17:30 19:00 NA Under mixed reservation algorithm, the reserved capacities will be determined by selecting an optimal result obtained either by pressure contribution based reservation algorithm or economic reservation algorithm. If more benefit can be achieved from storage reservation compared with the saving from response to energy price, the storage capacity reservation will be defined as financial driven and follow economic reservation result. Otherwise, the optimal result will stem from pressure contribution based reservation algorithm and justified as pressure contribution driven. Therefore, when the approaches of storage reservations are implemented in distribution system, future investment in network caused by network thermal limitation violation or voltage violation can be deferred. Conventionally, network reinforcement is required in three years. By employing the proposed method, it has been proved that network problem can be solved in the first eight years. The ability of distributed storage for network investment deferral will still last for a long period. In this demonstration, customers of Class 3 do not contribute to the identified network pressure, so the total storage capacity operated by them can be used for responding to energy price. If the thermal limit violation firstly occurs at substation, all customers are capable of offering their storage capacities for pressure mitigation. 4. Conclusions This paper presents a solution to enable DNOs to reserve a certain amount of distributed storage capacity in microgrid to manage LV network. Based on identified overloading or overvoltage level in

6 158 Zhimin Wang et al. / Energy Procedia 103 ( 2016 ) network congestion, the reserved storage capacities for diverse customers are capable of being quantified through pressure contribution based reservation algorithm and economic reservation algorithm. The mixed reservation algorithm developed based on these two basic approaches provides an optimal choice for battery capacity reservation from both technical and economic sides. For the test system, the storage reservation is mainly conducted for domestic customers who are represented by Class1 and Class 2 customers for effective network pressure mitigation and reinforcement deferral. Appendix A. Table 2 (A). Parameters of the test network Feeder name Number of customers per feeder section Feeder length(m) Feeder Feeder Feeder Table 2 (B)Parameters of the Networks Transformer capacity (kva) 750 Transformer utilization (%) 48.4 Feeder thermal rating(kva) 204 References [1] G. Owen. and J. Ward., "Smart Tariffs and Household Demand Response for Great Britain," [2] B. Daryanian, R. E. Bohn, and R. D. Tabors, "Optimal demand-side response to electricity spot prices for storage-type customers," Power Systems, IEEE Transactions on, vol. 4, pp , [3] Z. Peng, Q. Kejun, Z. Chengke, B. G. Stewart, and D. M. Hepburn, "A Methodology for Optimization of Power Systems Demand Due to Electric Vehicle Charging Load," Power Systems, IEEE Transactions on, vol. 27, pp , [4] Q. Kejun, Z. Chengke, M. Allan, and Y. Yue, "Modeling of Load Demand Due to EV Battery Charging in Distribution Systems," Power Systems, IEEE Transactions on, vol. 26, pp , [5] Z. Wang, C. Gu, F. Li, P. Bale, and H. Sun, "Active Demand Response Using Shared Energy Storage for Household Energy Management," Smart Grid, IEEE Transactions on, vol. PP, pp. 1-10, [6] W. Zhimin, L. Furong, and L. Zhenjie, "Active household energy storage management in distribution networks to facilitate demand side response," in Power and Energy Society General Meeting, 2012 IEEE, 2012, pp [7] B. M. Weedy, B. J. Cory, N. Jenkins, J. Ekanayake, and G. Strbac, Electric power systems: Wiley, [8] WesternPowerDistribution, "LV Network Templates Data," Bristol2012. [9] ENA. (2010). Common Distribution Charging Methodology. Available: [10] A. Henley and J. Peirson, "Time-of-use electricity pricing:: Evidence from a British experiment," Economics Letters, vol. 45, pp , [11] Elexon, "Load Profiles and Their Use in Electricity Settlement," ed.