Dynamic pricing based on a cloud computing framework to support the integration of renewable energy sources Rajeev Thankappan Nair1, Ashok Sankar2 1
Department of Electrical Engineering, College of Engineering, Trivandrum, Kerala, India Department of Electrical Engineering, NIT, Calicut, Kerala, India E-mail:
[email protected] 2
Published in The Journal of Engineering; Received on 1st September 2014; Accepted on 30th October 2014
Abstract: Integration of renewable energy sources into the electric grid in the domestic sector results in bidirectional energy flow from the supply side of the consumer to the grid. Traditional pricing methods are difficult to implement in such a situation of bidirectional energy flow and they face operational challenges on the application of price-based demand side management programme because of the intermittent characteristics of renewable energy sources. In this study, a dynamic pricing method using real-time data based on a cloud computing framework is proposed to address the aforementioned issues. The case study indicates that the dynamic pricing captures the variation of energy flow in the household. The dynamic renewable factor introduced in the model supports consumer oriented pricing. A new method is presented in this study to determine the appropriate level of photovoltaic (PV) penetration in the distribution system based on voltage stability aspect. The load flow study result for the electric grid in Kerala, India, indicates that the overvoltage caused by various PV penetration levels up to 33% is within the voltage limits defined for distribution feeders. The result justifies the selected level of penetration.
1
Introduction
Electric power generation using consumer-owned grid connected solar photovoltaic (PV) and wind generators is increasing significantly at the domestic level. As the penetration levels of renewable sources rise in the domestic sector, there may be time periods of power flow from the households into the grid during a day [1]. Under such a scenario, pricing methods that take into account the bidirectional energy flow situation are suitable. Additionally, the intermittent characteristics of renewable energy sources cause power fluctuations in the grid. In a grid connected system, utilities prefer to use incentive-based demand side management (DSM) programmes such as load shifting to avoid power fluctuations in the grid and improve stability. Incentive to the consumer via pricing aligned with the load shifting operations encourages DSM programme in the domestic sector [2]. Currently, residential users equipped with renewable energy generation facilities are subjected to either feed in metering or net metering. In the feed in metering system, all the energy generated is fed into the grid through a dedicated meter and is valued by a feed in tariff (FIT) determined by the regulatory body. This is generally called feed in tariff system. At the same time, energy consumed by the consumer will be metered through a utility meter and billed at consumer tariff. FIT generally adopts high tariff rate. The FIT system has been introduced by many countries, like Germany, USA and Italy, as a promotional measure to bring consumer involvement in the renewable energy development programme, such as grid connected roof top solar PV system installation. As the PV system installation at the domestic sector has attained a sustainable level, net metering has been introduced to promote self-consumption of renewable generation. A net metering mechanism allows a two-way flow of electricity wherein the consumer is billed only for the net electricity (total consumption-own production) consumed at retail tariff rate. Present net metering system is not supporting real-time operations, whereas real-time operations are needed in the metering system to support incentive-based DSM programme which uses dynamic pricing to provide incentive for adoption of the DSM programme in the domestic sector [3–5]. The high-level penetration of renewable energy sources in the domestic sector brings new operational challenges of handling and J Eng 2014 doi: 10.1049/joe.2014.0239
processing a huge amount of time series data at the household and utility level in the implementation of a dynamic pricing for the DSM programme. A metering mechanism capable of handling time series data generated from the renewable energy sources is needed at the household and a suitable platform that is capable of storing a significant amount of data, and having fast processing and good computational capability, is required at the utility level to adequately [6] monitor and verify the metering process on the consumer side. In these scenarios, dynamic pricing method using real-time data based on a cloud computing framework helps to reduce the difficulties because of operational issues [7, 8]. The time series data generated from renewable energy sources can be stored with minimum hardware in the virtualised storage environment of the cloud. The timely data retrieval and computational requirement related with the pricing can also be materialised [9, 10]. In this paper, a dynamic pricing method and a metering system suitable for the domestic sector with the large-scale grid connected renewable energy sources are presented. An hourly pricing model, on the basis of generation and demand, is formulated. The dynamic renewable factor introduced in the model is suitable for price-based DSM programme. The dynamic renewable factor-price relationship in the pricing methodology is presented in a simple IF_THEN rule format. The proposed dynamic pricing mechanism adopts real-time mode operations, thereby avoiding the requirement of forecasting. The cloud computing-based solution provides a platform for online, offline computations, monitoring and analysis of large data. This paper is organised as follows. Section 2 describes the development of the proposed dynamic pricing system model. The results and discussion are presented in Section 3 and this paper is concluded in Section 4. 2
Proposed dynamic pricing system model
The dynamic pricing is based on real-time data, so the approach eliminates the inaccuracy in the forecasting method. The dynamic pricing scheme uses energy pricing on an hourly basis. The basic idea behind the dynamic pricing is that the electricity price per kilowatt hour (kWh) of the consumer varies either by the demand or by the renewable generation in the household. In this system, electricity price of consumer at any time frame depends on the net difference between energy demand
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and generation of renewable energy in a household. In the proposed pricing model, there are two important prices in determining the energy price of the consumer at any time frame; the import price and export price. A unit of electricity imported from the grid has a value set by the import price whereas a unit of electricity that is export to the grid has a value set by the export price. The import price can vary in the scheme and the value is set by the utility. The dynamic renewable factor introduced in the pricing model supports DSM programmes. The utility concerns of monitoring and verification of time varying pricing structures and the infrastructural need of dynamic pricing schemes are addressed with cloud computing framework. 2.1 Pricing model The proposed pricing model incorporates capital cost related parameters, performance parameters and financial parameters associated with the generating plant for energy price calculation both in export and import conditions.
2.1.1 Power export charges: The model for energy charges in Rs./ kWh for the export of energy into the grid is formulated based on the guidelines issued by the central electricity regulatory commission [11]. To incentivise the consumer, the actual unit cost of renewable energy generated corresponding to each unit of energy exported into the grid is provided to the consumer. Following the cost recovery principle, installation costs made by the residential producer and utility need to be recovered as well. The following consideration is made on the cost component relating to the renewable energy source integration at the distribution level. The integration of renewable energy sources places additional burden on utility. The localised generation from renewable energy sources in the residential sector may lead to grid reliability problems, triggering additional investment by the utility for the corrective measures. The additional expenditure incurred by the utility to accommodate a large number of renewable sources need not be considered for the calculation of export charges from the household because of the following reasons. With the integration of renewable sources in the residential sector, utility is benefitted by avoiding transmission and distribution (T&D) cost and reducing transmission losses as a result of localised generation. The benefits to the utility from the local production are acting as a mode of compensation for the investment charges incurred by remedial measures to accommodate renewable energy sources. The price model to determine the energy price for export of energy from a consumer premises equipped with different types of renewable energy sources is formulated here. Consider a distribution system consisting of N number of consumers. Consumer premises are equipped with a number of renewable energy sources denoted by m. The energy price, which accounts for the capital as well as the operating costs associated with the renewable energy systems, is considered to determine unit price for the export of energy. It is calculated as m
Epexport
(Ak + Ok ) = m IC × 8760 × CUFk × (1 − dk ) k k=1 k=1
(1)
Epexport is the unit cost of energy for export in Rs./kWh. Ak is the annualised capital cost, Ok is the operation and maintenance cost of kth renewable energy system in rupees. ICk is the installed capacity of the kth renewable energy unit in kilowatt (kW), δ is the derating of the machine, and CUF is the capacity utilisation factor. The locational and time dependency of renewable energy production is brought into consideration by incorporating the site specific parameter (CUF) in the computation of unit energy price for export of energy. The annualised capital cost is calculated as Ak =
k
Ck × CRFk
(2)
where Ck is the capital cost and CRFk is the capital recovery factor, respectively, of the kth renewable energy system [12]. CRFk is a function of discount rate (d) and life period of the plant (γ). The capital recovery factor can be represented in (2) CRFk =
d(1 + d)gk (1 + d)gk − 1
(3)
Renewable energy plants are characterised by the low-cost operation and maintainance. The operation and maintainance cost of a plant mainly involves the administrative and general expenses, employee cost, repairs etc. Average or rate method can be used for calculation of operation and maintenance cost [13] Ok =
a × y × ck × (1 + esf )sk
(4)
k
where esf is the escalation factor, α is the rate of maintenance, y is the part of capital cost for which operation and maintenance cost is applicable and σ is the time period considered for operation and maintenance cost calculation. 2.1.2 Power import charges: The energy price, which accounts for the capital as well as the operating costs associated with the conventional generating systems, is considered to determine unit price for the import of energy. The above cost comprises two parts, namely fixed cost and variable cost. Power import charges enable utilities to recover fixed and variable costs plus a fair rate of return. Fixed costs are independent of short-term demand variability, including T&D capital expenditures. The fixed cost consists of (i) return on equity, (ii) interest on loan capital, (iii) depreciation, (iv) interest on working capital and (v) operation and maintenance cost. Return on equity: Return on equity (x1) is a measure of a utility’s ability to generate additional value for its shareholders. Return on equity is computed in rupee terms, on the equity base (E) decided for the generating station. The rate of return in percentage is given by RoR = PAT × (100/NW), where PAT is profit after tax and NW is the net worth x1 = E ×
RoR 100
(5)
Interest on loan capital: The cost of debt is measured by interest on loan capital. Interest on loan capital (x2) is given by x2 =
l
(Li × rLi )
(6)
i=1
where Li is the loan amount of the ith loan, rLi is the rate of interest of the ith loan and l is the total number of loans. Depreciation: Depreciation (x3) is the loss of value of an asset and it depends on the size, type and useful life of the plant. It is computed from the date of commercial operation of generating station x3 =
B
(tvi − svi ) × rdpi
(7)
i=1
where tvi and Svi represents initial value and salvage value of the ith asset, respectively, rdpi is the rate of depreciation and B is the total number of assets. Interest on working capital: Working capital is the amount needed to cover the cost of operating the generating company. The working capital is determined based on fuel stack, inventory of maintenance spares and one month operation and maintenance cost depending on
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J Eng 2014 doi: 10.1049/joe.2014.0239
the type of generating station. The total interest on working capital (x4) is given by x4 =
C
(wi × rwi )
(8)
where Gn(t) and Dn(t) represent the total renewable energy generated and consumer demand, respectively, in each time slot. The value of Dn(t) will be recorded by the smart meter in the consumer premises. Energy provided by a renewable source at any time frame is gk(t). Total energy produced in each time slot is given by
i=1
where wi is the value of working capital for the ith item, rwi is the rate of interest of the ith item. Ψ is the total number of items considered under working capital. Here, bank rate for investments is considered for calculation. Operation and maintenance cost: Operation and maintenance cost (x5) includes operation and maintenance cost of generating plant (O&Mplant) and that for renewable integration (O&Mimport). The O&M cost for the plant generally depends on the capacity of the plant. It consists of expenditure on spares, repairs, insurance and other overheads. Average method is used to determine the operation and maintenance expenditure for the plant. The integration of renewable energy sources to the existing grid structure results in an additional component O&Mimport. Additional expenditure incurred in the operation and maintenance of equipments used to integrate renewable energy sources to the existing grid is considered in this category. Variable cost: Variable cost is the fuel cost for each unit of energy generated. It depends on the fuel price and quantity of fuel used. The cost of fuel varies with the type of fuel, the calorific value of fuel and the availability and transportation charges. Variable cost (x6) is given by x6 = wf × Qf
(9)
where wf is the price of fuel and Qf is the quantity of fuel. The unit energy price (Ep) in Rs./kWh considering the fixed and variable cost is given by Ep =
(x1 + x2 + x3 + x4 + x5 + x6 ) IC × 8760 × PAF
(10)
where IC is the installed capacity and PAF is the plant availability factor. PAF in relation to a generating station for any period means the average of the daily declared capacity for all the days during that period, expressed as a percentage of the installed capacity in megawatt (MW) reduced by the normative auxiliary energy consumption. In general, unit energy price for import of energy for distribution system with conventional and renewable energy sources is given by (11) Epimport =
z j=1
z j=1
Vecj +
Vepj +
h j=1
h j=1
Decj
(11)
Depj
where z is the total number of conventional generating sources, h is the total number of renewable energy sources, Ωecj, Δecj are corresponding energy costs and Ωepj, Δepj are corresponding energy productions. The dynamic renewable factor is introduced in the model to modify the hourly pricing based on the export and import conditions. 2.1.3 Dynamic renewable factor: To support power management operations such as load shifting, the hourly pricing should have the flexibility to exactly capture the status of energy flow in the consumer premises. At times, when renewable generation exceeds the demand, consumer shall be benefited more as an incentive to promote the renewable energy. To incorporate the above features in the pricing model, the dynamic renewable factor βn(t) is introduced
bn (t) = Dn (t) − Gn (t) J Eng 2014 doi: 10.1049/joe.2014.0239
(12)
Gn (t) =
m
gk (t)
(13)
k=1
Now the price at any time slot is a function of βn(t). Let En(t) be the price at any time slot, and En is the unit energy price in Rs./kWh. The value of En varies between the export (Epexport ) and import (Epimport ) charges. The aggregated energy charges for a day, ET, is given by (15) En (t) = En xbn (t) T En (t) ET =
(14) (15)
t
The dynamic renewable factor becomes positive or negative in magnitude, corresponding to the power import and export conditions. In this pricing methodology, consumer receives price for energy during the time of energy injection to the grid. The consumer is charged with import rate of electricity during the time of energy consumption from the grid. The dynamic renewable factor used in the dynamic pricing scheme is used to capture and represent the dynamics of energy flow in the consumer premises. On the basis of the value of βn(t), the hourly electricity price of the consumer is determined as described in the algorithm (see Fig. 1). 2.1.4 Dynamic pricing algorithm: The algorithm to compute the hourly pricing (described in Fig. 1) is as follows. Let Dn(t) denote the demand of the consumer at any time frame, recorded by the smart meter at the consumer premises. The data handling service in the architecture forwards the aggregated generation level, Gn(t), in each time frame to the data storage module. The utility will send price details to the client side. The current value of the βn(t) is computed from step 5. The value of βn(t) varies between positive and negative magnitudes, which represent net import and net export, respectively, of energy. On the basis of the value of βn(t), the hourly electricity price of the consumer is computed. The aggregated hourly charges for a day are sent to the utility. Java programs were used to implement the algorithm in the cloud setup. The dynamic pricing can be accomplished through a pricing module on the client node of the cloud computing framework, which is envisaged for the smooth functioning of future electric grid. A cloud-based solution for the implementation of the pricing methodology and the role of cloud computing, in general, including pricing service, for the smart grid environment integrated with large number of renewable sources is illustrated in the next section. 2.2 Cloud computing framework Dynamic pricing programmes are highly dependent on the operational flexibility and infrastructural requirements such as large-scale data storage and processing. The need for large-scale real-time computing, communication, transfer and storage of data for dynamic pricing programmes is expected to be addressed by cloud computing [14]. A recent report [15] indicated that the flexibility and scalability of storing and processing of large amounts of data and cost savings are some of the major benefits of using cloud solutions for pricebased DSM applications. The cloud environment provides a flexible way of building and facilitating computing and the storage infrastructures for varying online and offline services. A cloud computing model for managing the real-time streams of smart grid data for the needs of the different energy market participants is presented in [16]. This paper discusses smart grid data management based on
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Table 1 Hardware details of test bed Item cloud controller client node
Fig. 1 Price depending on generation and consumption level
specific characteristics of cloud computing, such as distributed data management for real-time data gathering, parallel processing for real-time information retrieval and ubiquitous access. The service oriented architecture in the cloud computing platform is proposed in this paper to address the data storage and processing requirements of the dynamic pricing programme. The architecture is a combination of a data storage module and various services modules. The architecture encompasses the data storage module, data handling service and pricing module. The data storage module was realised in the virtualised storage environment of the cloud. Data handling service is meant for manipulations of generation and consumption data from various generating sources and loads. Computation of electricity price of consumer on time basis is carried out by the pricing module. The dynamic pricing operations are implemented through the client node on the consumer side. The details of specifications of the cloud test bed are presented in Table 1. The functional blocks to realise various services, associated data coordination and processing are depicted in Fig. 2. Generation facility
Recommended hardware HpProliant, 64 bit x86, 2048 K cache,12 GB random access memory (RAM), 2 × 1 TB HDD and 1 GB NIC Intel Pentium4 3.20 GHz, 2048 cache, 8 GB RAM, 1 TB HDD and 2 × 1 GB NIC
is available with the consumer and Zigbee integrated smart meters transmit the hourly generation and consumption data to the data storage module. Wireless communication is envisaged as a medium of communication between the consumer and client node and internet technology for the data flow between the utility and client node. The pricing module is where logic of the hourly pricing resides. The module executes the pricing service taking into account the local generation in each timeframe. The pricing module and associated functions can be accomplished through a client node on the consumer side. The advantage of this method is the reduced amount of data exchange between the consumer and utility. The hierarchy in the architecture permits the utility to change hourly rate of energy. Thus, the utility has the flexibility to change the electricity price at times of peak demand. The framework considers a general multitier application consisting of M tiers. The assumption is that each tier runs on a separate virtual machine Vm(m = 1, …, M). The framework is formulated to support price-based DSM programmes which require consumer interaction. Consider workload with transaction mix (α1, α2, …, αn), where αn is the average request rate of transaction type n during one time period. The central processing unit (CPU) consumption Cm at tier m can be defined as a linear function of transaction mix as follows Cm =
N
Dnm ∗an
(16)
n=1
where Dnm is the average CPU demand of transaction type n at tier m. 2.3 Simulated case study The proposed pricing methodology was illustrated using a case study carried out in Thiruvananthapuram district of Kerala, India. The hourly load profiles of households in the district were considered for study. Typical study was conducted for Kerala. The income level of
Fig. 2 Architectural framework for flexible pricing This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/ licenses/by-nc-nd/3.0/) 4
J Eng 2014 doi: 10.1049/joe.2014.0239
Table 2 Important parameters for tariff calculation Item
Level
CUF degradation factor useful life discount factor
Fig. 3 Hourly load profile
people is such that penetration of air conditioners has increased tremendously in Kerala. As a result, load during day is increasing in Kerala. Hence, the domestic load profile of a high end consumer was considered for the study. A typical load profile is given in Fig. 3. The latitude and longitude of Kerala is 10.85 N and 76.27 E, respectively. The district receives an average number of clear sunny days of about 300 and daily average solar radiation of ∼ 5.2 kWh/ m2/day. Hence a solar rooftop PV was selected as the renewable source for the self-generation source used in the simulation. Hourly variation of solar energy at the location is depicted in Fig. 4. The important parameters considered for the calculation of unit cost of generation (export charges) are given in Table 2. For a solar PV system, capacity utilisation is the ratio of the actual electricity output from the PV system to the maximum possible electricity output during the year. The estimated output from the solar PV system depends on the design parameters, and the same was calculated using standard software. The capacity utilisation factor of solar PV system depends on site specific parameters and technical conversion efficiency of different components. The computed value of 18% was used in the energy price calculation. Considering the nature of the solar technology available, an annual degradation factor of 0.5% was applied on the energy production. The values of other parameters were selected in accordance with the guidelines issues by Central Electricity Regulatory Commission for the unit energy price calculation in cases of energy injection into the grid. The discount factor proposed by the Kerala State Electricity Regulatory Commission to determine the tariff for solar roof top system is 13.3%. We have used the value for computation. The export and import charges of electricity for the household case were determined using the modelling explained in Section 2. The computed values of the export and import charges are Rs. 8.45 and 4.5, respectively. Simple constant efficiency model is used for solar panel. The energy generated by the PV array is given as EPV = η × Ap × IT, where η is the PV system efficiency, IT is the total radiation incident on the array (watt per square metre) and Ap is the total array area (square metre). The study considered 2 kW rating solar PV.
18% 0.5% 25 years 13.3%
Basically, two-way information flow is needed for the dynamic pricing operations. Two-way information flow facility is required between the smart meter and client node, and also between client node and utility. The two-way information flow was incorporated in the simulation study as explained below. In the simulation setup, the consumer and utility blocks are modelled as databases. The hourly data in the generation and consumption profiles were stored in the databases created as multiple instances, each one on its own virtual machine. To ensure that the simulation is realistic, actual data related to the consumption and generation profile of a region were used in this study. The computational work such as aggregating hourly power consumption and generation values was executed through the data handling service and pricing calculation through real-time pricing module, created as multiple instances in the computing framework. Although, the method used in the simulation is an approximation of data sending and receiving by smart meters on the side of consumers and utility, it takes into account all the required components and dynamics that are expected to be used in the PV integrated consumer structure. Simulation experiment was conducted to evaluate the effectiveness of the proposed pricing methodology in the test bed in the open stack cloud environment. 3
Results and discussion
Under the dynamic pricing, consumer’s hourly energy price varies according to the net power flow condition in the household. Fig. 5 illustrates the hourly variation of electricity price. The result indicates that the power export and import conditions in the household are reflected as variations in the hourly electricity pricing recorded by the metering mechanism which is incorporated into the pricing module. The hourly electricity price becomes negative during a few hours in a day, which indicates that energy generated in the household exceeds the demand and consumer is benefitted in terms of economic aspects. The economic benefit to the consumer equipped with solar PV system under net metering and dynamic pricing for an increasing PV to load ratio is analysed in this section. The PV systems sized to meet varying percentages (10, 20, 30, 40 and 50%) of the consumer’s annual consumption are referred to as PV to load ratio. The percentage savings in annual energy charges of the consumer for different values of PV to load ratio are given in Fig. 6. Here the percentage savings represent the annual bill savings of the consumer
Fig. 4 Hourly solar generation profile J Eng 2014 doi: 10.1049/joe.2014.0239
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Fig. 5 Hourly variation of energy price
Fig. 7 Load curve
(with PV) under net metering and dynamic pricing in comparison with the base line case (fixed tariff without PV). The above result indicates the benefit of a consumer equipped with self-generation facility in the household. The comparison of annual bill savings under the proposed dynamic pricing in comparison with the existing net metering scheme is described in Table 3. The bill savings are expressed in terms of the calculated reduction in the annual household bill in comparison with the existing fixed tariff structure under net metering scheme. Although net metering scenario is not dynamic, the same was considered for comparison purposes since net metering scheme is adopted by different countries around the world for roof top solar PV system. As the PV to load ratio increases, the dynamic pricing becomes progressively more attractive relative to the net metering. As PV system increases in size, it increases the power injection into the grid and the case is well supported by the pricing structure in the dynamic pricing scheme. The case studies suggest that consumers will perceive the dynamic pricing tariffs as better value than the fixed price tariffs. The fairness concern in the electricity pricing is well addressed in the dynamic pricing scheme. In the existing net metering scheme, the excess energy exported into the grid is credited at the retail tariff applicable to the consumer category. Since actual unit cost of solar energy generation is higher than the existing retail tariff
rate, the interest of the consumer is not promoted in this scheme. In the proposed pricing scheme, actual unit cost of renewable energy generation is considered for the power export and the existing retail rate for the power import conditions, and thereby the interest of both the consumer and utility is promoted equally in the pricing scheme. The case study of a typical polymer industry in the industrial park in the state of Kerala, India is used to compare the performance of the proposed dynamic pricing to industrial sector with the normal domestic area. The connected load of the industry is 100 kW with a contract demand of 80 kVA at 11 kV from Kerala State Electricity Board. Average daily electricity consumption is 838 kWh. The industry operates between 8AM and 6PM. Load curve of the industry for a typical day is shown in Fig. 7. The time-of-use (TOU) tariff is applicable to the industry. The demand charge is Rs. 270/kVA/month of billing demand, whereas the base energy charge is Rs. 3/kWh. Under this scheme, the energy charges followed by Kerala grid are in the ratio 1:1.5:0.5 for normal, peak and off peak periods. The comparison of annual bill savings under the proposed dynamic pricing in comparison with the TOU scheme is described in Table 4. Here the percentage savings represent the annual bill savings of the consumer (with PV) under dynamic pricing in comparison with the TOU tariff. As the PV to load ratio increases, the dynamic pricing become progressively more attractive relative to the TOU tariff. The results suggest that as PV system increases in size, the
Table 3 Benefits of applying proposed pricing to domestic consumer
Table 4 Benefits of applying proposed pricing to industrial consumer
Fig. 6 Comparison of savings in energy charges
PV to load ratio
15 30 45 60
Total annual bill
Bill savings
Net metering, Rs.
Proposed dynamic pricing, Rs.
Proposed dynamic pricing, %
40 716 33 288 24 732 16 656
40 435 31 512 22 104 13 975
1 5.2 10 16.1
Total annual bill
Bill savings
PV to load ratio
Industrial tariff, Rs.
Proposed dynamic pricing, Rs.
Proposed dynamic pricing, %
15 30 45 60
761 400 622 800 483 840 345 240
746 172 588 050 424 800 195 120
2 5.9 12.2 43
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J Eng 2014 doi: 10.1049/joe.2014.0239
Fig. 10 Hourly load demand profile with suitable level of solar PV system penetration in Kerala grid
Fig. 8 Peak solar generation for various penetration level
benefit to the industrial consumer under the dynamic pricing is more attractive than that for a domestic consumer. Dynamic pricing programmes are highly dependent on the infrastructural requirements such as data storage and computing. The electric system planners require the admissible level of penetration in the studied area to determine the future infrastructure requirement to handle the dynamic pricing programmes. Simple method to determine the appropriate level of PV penetration based on the voltage stability aspects is presented below. Studies [17, 18] have reported that high PV penetration can affect the voltage profile depending on the load condition and amount of PV penetration. The method used to determine the appropriate level of PV penetration is illustrated below. The admissible level of solar PV penetration is selected considering the stability point, so that the magnitude of power generated from the solar system at peak time (noon) should not be above the average demand of the studied area. The plan of National Solar Mission in India is to provide 20 000 MW solar installations by 2022. The focus has primarily been on grid connected rooftop PV systems. In this context, the definition of PV penetration used in the study is based on the peak demand in the domestic sector. PV penetration is defined as PV penetration (%) = Total PV generation (MWh) /System peak demand (MWh) Simulation experiments were conducted to determine the appropriate levels of penetration for the substation and the Kerala grid. To determine these appropriate levels, average domestic load profile of the electric grid in Kerala was considered. The details of the steps for the experiment in the case of a substation in Kerala consisting of 30 000 domestic consumers are given here. The penetration level was varied from 10 to 50 to study the generation profile corresponding to the different levels of penetration. The peak solar PV
Table 5 Demand support from solar PV system to the grid Area
single substation with (38% PV penetration) Kerala grid with (33% PV penetration)
J Eng 2014 doi: 10.1049/joe.2014.0239
Solar PV generation, MWh/day
Demand support from solar PV, %
Min, MWh
Max, MWh
7.5
26
141
32
820
1205
6570
26
generation corresponding to various levels of penetration to the substation is shown in Fig. 8. The intersection of the peak value of power generated from solar PV and average demand occurs at the level of 38% penetration. The value is selected as the appropriate level of penetration; a load flow study was conducted to verify the voltage stability aspects of this level of penetration. The result of the load flow study is depicted in Fig. 9, which indicates the quadratic behaviour of steady-state bus voltage. Although only a few of the bus voltage magnitudes are shown, the same quadratic type behaviour was observed in all the buses of the studied area. The overvoltage caused by various PV penetration levels up to 38% is within the voltage limits defined for distribution feeders. The result justifies the selected level of penetration. Solar PV system rating required to meet 38% PV penetration in the place considered is 22 000 kW. This can be achieved by providing 2 kW solar panel each to 11 000 consumers. Studies were extended to the electric grid in Kerala state, consisting of more than 7.5 million domestic consumers. The level of penetration for this case was determined as explained in the previous section. In this case, the overvoltage caused by various PV penetration levels up to 33% is within the voltage limits defined for distribution feeders. The improved load profile for the 33% solar PV penetration level is shown in Fig. 10. Table 5 provides the demand support from solar PV system to the grid for the above two cases. The result for the Kerala grid indicates that 33% solar PV penetration level in the Kerala grid provides 26% demand support to the grid. 4
Fig. 9 Steady-state voltage deviation of the system
Load
Conclusions
A dynamic pricing method based on a cloud computing framework, suitable for a domestic sector with the grid connected renewable energy sources, was presented in this paper. This paper also addressed the operational challenges in the implementation of the dynamic pricing in situations with bidirectional energy flow and intermittent renewable energy sources. The dynamic pricing and metering system was tested by the simulation of a real scenario of This is an open access article published by the IET under the Creative Commons Attribution-NonCommercial-NoDerivs License (http://creativecommons.org/ licenses/by-nc-nd/3.0/) 7
a solar PV system installed in a house, and the results indicated that the dynamic pricing captures the variation of energy flow in real time. The case study suggests that the dynamic renewable factor can capture and represent temporal variations of the generation and demand and is suitable for load shifting applications. It was demonstrated that the appropriate level of PV penetration in the distribution system, based on voltage stability aspects, can be estimated from the generation and consumption profiles of distribution systems. The result throws light on the policy formulations and the need of higher level of monitoring and verification with the large-scale integration of renewable energy sources. A simulation experiment was conducted to evaluate the effectiveness of the dynamic pricing operations in the open stack cloud computing framework. The study results indicated that with the addition of a simple client node to the cloud computing network of a utility, the storage and computational requirements for the proposed dynamic pricing method can be achieved at a minimal cost.
[6] [7] [8] [9] [10] [11] [12] [13]
5
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J Eng 2014 doi: 10.1049/joe.2014.0239
