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Design of load balancing mechanism for Indian electricity markets

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Abstract

The Indian electricity market is undergoing a major transformation to combat the issues of steadily rising demand, increasing energy shortfalls, and growing renewable energy penetration. Consequently, the Central Electricity Regulatory Commission (CERC) in India has proposed the introduction of ancillary services. Among the services, frequency support ancillary service is expected to have a significant impact on the grid operation. This ancillary service would ensure that the demand and supply are balanced at all times. However, the service proposed by CERC is designed as a contingency service and not a balancing service. In this paper, an alternative load balancing mechanism that serves the balancing needs of the Indian grid is proposed and analyzed. The main contribution of this study is the estimation of the costs and impacts of this mechanism using simulations based on data from the Indian state of Karnataka. The study is intended to foster effective ancillary services policies in India. The results show that the mechanism would reduce the number of real-time emergency events, which are used to avoid grid collapse, by around 55 % at an energy purchase cost increase of 3.5 %. This work illustrates the availability of cost-effective mechanisms for load balancing in the Indian power system, and also highlights the need for alignment of system stakeholder incentives.

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Notes

  1. Unscheduled interchange (UI) mechanism is used in real-time to manage deviations from day-ahead schedule. It is a frequency based pricing mechanism; the penalty/compensation is based on the power system frequency. For more details, please refer to A.1.

  2. Power pool is an electricity market design in which all market participants are required to schedule their transaction through the system operator. The system operator manages the transmission network and scheduling as well as electricity trading. Market participants with bilateral contracts enter as price takers. For more information, please refer to [66].

  3. Energy exchanges [66] are another form of electricity market in which participation is optional. It is a platform for only trading energy; the scheduling and dispatch is managed by the transmission system operator (TSO).

  4. The term ‘spot market’ is often used to refer to day-ahead as there is no real-time market. To avoid confusion, use of the term spot markets is avoided.

  5. Most of the states have unbundled their power system (albeit under government control), leading to dozens of power utilities, with many states having multiple distribution companies (DISCOMS) each with distinct geographic coverage.

  6. The official shortfall is rather low, due to a quirk in the methodology. The actual shortfall is likely several times higher. Reference: “Re-thinking Access and Electrification in India: From Wire to Service”, Rahul Tongia, Brookings India Discussion Note 01-2014.

  7. Net demand is defined as dispatch linked net demand, which is the actual demand (demand served + load curtailment) after removing non-dispatchable supply such as wind power. Appendix A.2.3 has more details.

  8. Shadow price represents the value of the Lagrange multiplier associated with a constraint in an optimization problem. In the context of this study, the shadow price represents the value of the next unit of electricity in the system.

  9. This is the dispatchable diesel capacity, and excludes diesel owned by end-users for back-up power.

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Acknowledgments

The authors would like to thank Karnataka Power Transmission Corporation (KPTCL), India and Southern Regional Load Despatch Centre (SRLDC), India for providing data for this study. Further, the authors would also like to thank Carnegie Mellon Academic funding for providing support for this study.

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Authors and Affiliations

  1. Carnegie Mellon University, 5000 Forbes Avenue, Baker Hall 129, Pittsburgh, PA, 15213, USA

    Hameed Safiullah

  2. ETL G24.2, ETH Zurich, Physikstrasse 3, 8006, Zurich, Switzerland

    Gabriela Hug

  3. Brookings India, D-64, 2nd Floor, Defence Colony, New Delhi, 110024, India

    Rahul Tongia

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Correspondence to Hameed Safiullah.

A Appendices

A Appendices

1.1 A.1 UI mechanism

Fig. 12
figure 12

Frequency based pricing, aka unscheduled interchange (UI) mechanism, in the Indian system. The compensation/penalty for unscheduled production or withdrawal is based on the real-time system frequency. The price at 49.95 Hz is intended to promote all coal generators to increase their production. And, the price at 49.7 HZ denotes the cost of producing power from the most expensive generators. Hence, when the frequency falls below 49.7 Hz, all generators can be expected to provide their maximum output [15]. An exchange rate of 1 USD = 60 Rs. is used

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The frequency based pricing mechanism - unscheduled interchange (UI)—was developed with an intention to maintain the power system frequency within a tighter band than before, now targeted at 49.7 to 50.2 Hz. The real-time price is expected to reflect the marginal cost of generators thereby encouraging generators to contribute to maintaining grid stability (see Fig. 12). When the frequency falls below 49.7 Hz, all generators are expected to feed in their maximum available output. Therefore, the maximum compensation is provided at the lower limit of the allowed frequency band. If not enough generation is available, the SLDCs have to reduce their demand or face hefty fines. Beyond this, when there is no additional generation available, states resort to feeder level load curtailment to avoid penalties.

1.2 A.2 Simulation data details

The TSO in the state of Karnataka, which is part of the southern regional grid, is chosen for the analysis. The details of the entities are as follows.

1.2.1 A.2.1 Dispatchable generators

Fig. 13
figure 13

Thermal-power capacity utilized is lower than installed capacity due to constraints on availability. The data is shown for year 2013

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Table 4 Generator description
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Table 5 Generator characteristic
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  1. (i)

    Coal-based generators The state-owned generators sell electricity to state load serving entities (LSE) based on energy purchase contracts. Similarly, the independent power producers (IPP) have energy purchase contracts with the state-owned utilities for a portion of their output. Due to the variability in power supplied to the state, the maximum generator output is modeled based on the daily peak output availability rather than the installed capacity for each simulated day. This also helps in incorporating outages faced by the generators. Figure 13 shows the maximum utilized capacity as opposed to installed capacity for days in year 2013. It is assumed that 90 % of the daily peak output is available throughout the day. The average energy purchase cost per unit (kWh) of electricity will be used in our unit-commitment and economic dispatch optimizations to choose the economically optimal set of generators to serve the demand. The energy purchase cost is based on annual financial reports [3] and regulator approved energy purchase cost [34] for a state LSE. The energy purchase cost along with installed capacity for the thermal generators is shown in Tables 4 and 5. Traditionally, load-following was an inherent part of vertical integrated utilities and the costs were seldom calculated exclusively. After electricity market restructuring, load-following became a separate service and costs had to be estimated. Intertek Aptech [33] have estimated the lower bound of load-following costs by using data from generators in North America. These cost estimates are used by matching the thermal generators to the stated description as shown in Table 5 (conversion rate of 1 US\(\$ =\) 60 Rs. is used). In addition to costs, generator characteristics are included in the optimization. The ability of a generator to provide load-following service is dependent on its ramp-rates (maneuverability). The ramp-rates used in this study are obtained from [5, 24]. The upper limit of the ramp-rates are used. Further, the other generator characteristics such as minimum output-level, minimum-off time and minimum-on time are obtained from IEEE RTS-96 [29].

  2. (ii)

    Hydro-power Hydro-power generators have high ramp-rates and are capable of very flexible operation, and these have low marginal costs of generation. Nearly a quarter of the state’s power generation capacity is composed of hydro-power but new capacity addition is limited due to geographic and social constraints. One major issue is that generator output is heavily dependent on water availability. The water availability in turn is dependent on various factors such as annual rainfall, irrigation schedules and seasonal demands. At present, the water available in dams after the rainfall season is almost equally partitioned for each day of the next year. Therefore, hydro-power is constrained by resource (water) availability in addition to capacity. Hence, constraints on daily available energy (GWh) in addition to capacity (MW) constraints are included. Figure 14 shows the daily energy used from hydro-power for the days of the year 2013. Though the allotment is equally partitioned, the usage is dependent on various operational factors and hence appears different for different days. There are about 80 hydro-power generator units in the state. Modeling each generator individually would be computationally intense. Since hydro-power generators are extremely flexible, the results for an aggregated model and for a model with individual generator would not be significantly different. Therefore, hydro-power generators are modeled as a single aggregated resource with a power purchase cost of 0.78 Rs./kWh [3]. For this study, the ramp-capability of hydro-power generators is limited to that of diesel generators. The generator is estimated to ramp up 15 % of its capacity per minute. Further, the flexibility of the generator enables it to cycle (switch on/off) in less than an hour. Due to high flexibility, the load-following cost is assumed to be negligible.

  3. (iii)

    Diesel generators The state possesses about 210 MW of generation potential powered by diesel.Footnote 9 Diesel generators are comparatively small sized generators that are often used as backup power. Unit cost of electricity produced is expensive when compared to other generators. The installed capacity and power purchase cost of diesel generators in the state is shown in Tables 4 and 5. On the positive side, these generators have very high ramp-rates and are designed for handling varying output levels. Therefore, they would not incur any additional load-following costs from an operational perspective.

  4. (iv)

    Central generation stations Central generations stations (CGSs) are generators that provide electricity to multiples states. Though the state can request up to its allocation and even more if available, the state will not have visibility of its overall operation as the generators serve multiple states. Therefore, these generators can be scheduled for energy but cannot be dispatched by the states for load following. The state’s share of the central generation stations amount to about 1900 MW [39], and the power purchase cost is between 2 and 3.1 Rs./kWh [3, 34].

  5. (v)

    Captive power plants Captive power plants are usually part of a larger industrial operation with a primary objective other than producing electricity. Therefore, similar to CGS, captive power plants will be able to schedule energy but not provide load-following in order to avoid interference with the primary operation. The state has about 350 MW of captive power plant capacity with an average power purchase cost of 3.85 to 5.34 Rs./kWh [3, 34].

  6. (vi)

    Load curtailment Energy shortage has been a chronic issue plaguing the Indian power system. This issue has resulted in significant amounts of load curtailment (aka load shedding) in the state. Though the installed capacity appears to be greater than the load served, there is high uncertainty in power availability due to fuel shortages and generator outages. Further, a significant portion of the generation is non-conventional energy resources with high uncertainty and variability. This worsens the generation availability and controllability. During energy shortages, as a last resort, power system operators use load curtailment to avoid severe grid outages. This is considered as a measure of last resort and, for the base calculation, valued higher than any generator available in the system at 18 Rs./kWh.

Fig. 14
figure 14

Daily energy output of hydro-power for the year 2013. The measureable decrease and increase corresponds to pre-monsoon and monsoon periods

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1.2.2 A.2.2 Non-dispatchable generation

Renewable energy resources such as wind and solar are considered non-dispatchable due to the uncertainty and variability in their power output, and are modeled as negative demand. Wind energy is the predominant source of renewable energy in the state. The state currently has a planned capacity of about 4000 MW with energy purchase cost of 3.3 to 3.85 Rs./kWh [3, 34]. These costs are excluded from the calculations as they would be the same across all cases independent of the optimization procedure.

1.2.3 A.2.3 Demand data

The objective of the system operator is to serve the system demand at least cost while ensuring system stability and reliability. As several generation resources are non-dispatchable, net-demand is used in our optimization procedures.

$$\begin{aligned} {\textit{Net Demand}}={\textit{Demand Served}}+{\textit{Load Curtailment}}-{\textit{Wind output}} \end{aligned}$$

The demand served (5-min interval) data is obtained from the Southern Regional Load Dispatch Center (SRLDC). And, load curtailment data, which represents the actual curtailment in the system, was obtained from the state load dispatch center (KPTCL). Finally, the non-dispatchable (wind) generation data was obtained from the state transmission system operator (KPTCL). The average hourly demand is used for the day-ahead market and the 5-min data for the real-time market.

Fig. 15
figure 15

Net demand data for year 2013. Net demand is obtained by subtracting wind output from actual demand (demand served + load curtailment) in the system

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Safiullah, H., Hug, G. & Tongia, R. Design of load balancing mechanism for Indian electricity markets. Energy Syst 8, 309–350 (2017). https://doi.org/10.1007/s12667-016-0199-3

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  • DOI: https://doi.org/10.1007/s12667-016-0199-3

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