Serving Customer Energy Services using Contracts and Load Management Programs

Kah-Hoe Ng Gerald B. Sheblé

Student Member Fellow Member

Department of Electrical and Computer Engineering

Iowa State University, Ames, Iowa

Abstract: Deregulation in the power industry has encouraged the utilities to change their practice, from regional monopoly to open market competition. Additionally, these utilities are also restructured to meet the supply and the demand for electricity using energy contracts traded in the open market. Load management programs were initiated in the 1970s. The programs actively influence customer demand to improve scheduling flexibility for the utilities. The programs, despite their initiation in time of regulation, deserve attentions in time of deregulation. In this paper, a profit-oriented utility, without any generation capability, uses energy contracts to provide customer energy services in a competitive environment. In addition, this paper further shows how load management programs can improve the profitability of serving customer energy services.

Keywords: deregulation, energy contracts, auction market, load management programs, direct load control (DLC).

I. INTRODUCTION

Calls for competition in the power industry, from the wholesale to the retail level, has made deregulation an attractive option around the world. A study of this new evolving market structure reveals the need for a more acceptable framework that would ultimately satisfy regulatory bodies, customers, and suppliers, alike. One approach is the application of a brokerage system to the power industry to promote competition. To accomplish this, existing vertically integrated utilities need to be broken up. The framework of an energy market can be found in [1].

The deregulation in the power industry provides many research opportunities. One of these opportunities is studying how the regulated power industry should re-strategize itself to operate in a deregulated, competitive environment. In particular, this paper emphasizes on energy services, illustrating how energy services can be provided to customers using energy contracts and load management programs.

In section II, auction market and energy contracts are discussed. Contract specifications to be used for illustration are also presented. Section III reviews the load management programs. Direct load control (DLC) programs that will be used for illustration are also presented. Section IV, V and VI provide examples, illustrating how utilities may have purchased energy contracts to meet customer demand. In addition, the benefits of load management programs are also identified in the example. Finally, section VII concludes this





paper, emphasizing the feasibility of serving customer energy services using energy contracts and load management programs.

II. AUCTION MARKET AND ENERGY CONTRACTS

Auction market provides a competitive environment where energy contracts may be traded freely. Dekrajangpetch [2] characterizes an energy auction market based on how the market is implemented. First is centralized daily commitment auction (CDCA), requiring all involved market participants to submit their aggregated demand and cost function for a specific duration versus single period commodity auction (SPCA), requiring all involved participants to submit their incremental demand and price for a particular period. Second is single-sided auction, where only the buyers or the sellers can submit their offer price, versus double-sided auction, where all participants are allowed to submit their offer price. Third is uniform pricing, where all buyers pay and all sellers receive the same price, versus discriminating pricing, where sellers get paid and buyers pay according to their bids. Fourth is heterogeneous product, where all contracts contain product of equal quality, versus homogeneous product, where quality of the product differentiates the contracts. In the power industry, ancillary services are the qualities that differentiate the energy contracts. In this paper, the auction mechanism is assumed to be a heterogeneous, discriminating, and double-sided SPCA.

Given assumptions on the auction mechanism, several important aspects remain to be addressed. First aspect is on how the heterogeneous energy contract is defined in this research. Second aspect is on how the utilities may trade in the auction market. Third aspect is on how the traded energy contract is defined.

Kumar [3] outlines six ancillary services that distinguish the energy contracts. However, only the reliability aspect will be addressed, i.e., how reliable is the energy that is delivered to a buyer. 95% reliable energy for example, means that all the purchased energy will be delivered to the utility that purchases the contract 95% of the time while no energy will be delivered 5% of the time. Figure 1 shows a contract of 5 MWhr having a 95% reliability level. In addition, contract buyers, especially the utilities that serve customer energy services, need to be aware of the volatility in customer demand. Volatility refers to the percentage change allowed in the demand during a specific duration. For instance, if a utility purchases an energy contract allowing 5% volatility, the customer demand can fluctuate within the 5% range when energy is delivered. Figure 2 shows a contract of P MWhr allowing b volatility level during delivery duration. Any customer demand exceeding level will not be served under the contract specification. In addition, when customer demand falls below , contract seller does not have to compensate the contract buyer for the amount of energy not delivered.

Figure 1. Reliability level of a contract delivering 5MWhr @ 95% reliability level.

Figure 2. The customer demand during a particular duration.

In the assumed auction mechanism, market participants may trade energy through four different markets [1]. These are the spot market, the forward market, the futures market, and the options market. In the spot market, the energy for next-day delivery is traded. In the futures, forward, and options markets, the contracted energy is to be delivered in the future, ranging from within one month to several years from the date the contract is issued. The existence of the futures, forward, and options markets is to allow both buyers and sellers of energy to lock in energy prices in order to reduce the risk of business operation. A forward market is less liquid than a future market. Specifications in a forward contract are tailored to meet the needs of both the contract seller and buyer. The bilateral contract that has been used in the power industry for the past few decades is synonymous to the forward contract. A futures contract, however, is tailored to meet the needs of most players in the market. It often ends up with a cash settlement instead of actual delivery of the contracted product. In the options market, the utility serving customer energy services is given the right to sell or buy energy (of which the contract specifications are similar to those in the future market) in the future.

To purchase the energy, a utility serving customer energy services needs to decide (1) the price of energy, (2) the quality of the energy, (3) the time of delivery, and (4) the duration for which the energy is to be delivered. The price of energy is determined based upon the demand and supply of the market. Assuming that the auction market is perfect, the price of energy will reflect the cost of generation. Again, the quality of energy is equivalent to how reliable the energy is in this research. The time of delivery is equivalent to when the energy will be delivered to the destination requested by the contract buyer. Figure 3 shows a sample of a utility’s demand and the different energy delivery duration the utility could have purchased. Type 1 duration lasts less than an hour. It is particularly useful when the peak demand is far shorter than an hour. Type 2 duration lasts for an hour. Type 3 duration lasts for a day. Type 4 duration lasts for a month. Type 5 duration lasts for more than a month. In the existing energy markets, Pennsylvania-New Jersey-Maryland (PJM) and California PX for instance, Type 2 and Type 4 duration are most common, where Type 2 duration is traded in the spot market while Type 4 duration is traded in the futures and options market. In existing markets, Type 4 duration refers to delivery during peak hours of the day only. Because of the difference in the delivery duration between the spot market and the FFO market, the FFO price is the combinatorial effect of the spot prices in multiple periods.

After a contract is agreed upon, there will be occasions when the contracts may be broken. Specifications need to be established as to when contract buyers should be compensated for any defaulted contracts. Ng [4] lists various policies when the contract buyers should or should not be compensated.

With the different markets and contract specifications defined, examples of various contracts are presented in Table 1. These include the contracts from the different markets, contract specifications, the amount of contracts traded, and the contract prices. An option contract requires a non-refundable premium for the right to purchase or sell. This premium is not required in other types of markets.

III. LOAD MANAGEMENT PROGRAMS: PAST AND FUTURE

Generally, load management programs can be categorized into direct load control (DLC), indirect load control (IDLC), and energy storage system (ESS). The DLC allows the utilities to shed remote customer demand unilaterally. IDLC allows the customers to control their demand independently according to the price signals sent by the utilities serving the energy services. Both DLC and IDLC share the same concept, where controllable demands are shifted to the future where the economic values are higher. However, they differ on who has the ultimate control on the electric appliances. Finally, ESS allows both the utilities and the customers to store energy during low-cost sessions and consume during high-cost sessions. References [5,6] are among all work reporting on the technological development of load management programs.

Prior to deregulation, load management programs are used to minimize the cost of operation (cost-based) or the peak demand (load-based) [4]. However, cost-based model or load-based model does not guarantee maximum profit. A direct consequence from encouraging competitions in a deregulated energy market is the profit-oriented business operation. Thus, in a deregulated energy market, a profit-oriented load management program is a natural progress. Ng [4] further discusses the impact of deregulation on load management programs. In this paper, the load management programs are used to improve the utility’s profitability in serving the customers energy services. The utility uses the load management programs to lower the volatility of the customer demand and to supplement the customers’ desired reliability level.

Since their inception, numerous algorithms and models, linear and nonlinear, for load management programs have been proposed. Algorithms and models that utilize linear programming can be found in [7–10]. In particular, the load management programs’ models presented in [7] are used. In this paper, only the DLC model is used for illustration. The model uses linear constraints and linear objectives to characterize the controllable customer demands based on: the impact on customer demand when energy is deferred/paid back, the cost associated with the controllability, and the impact these controllable customer demand had on the inflow of revenue.

Table1. Examples of contracts in the auction market.

Market		Spot	Future	Option	Forward
Position		Buy	Long	Long call	Long
Number of contract		5	10	2	5
Size/contract		5 MW/hr	5 MW/hr	5 MW/hr	12MW/hr
Premium		−	−	$5.00	−
Delivery price		$25.00	$23.00	$20.00	$25.00
Delivery duration	Begin	1/1/00 9:00 a.m.	1/1/00 9:00 a.m.	1/1/00 9:00 a.m.	1/1/2000 8:00 a.m.
	End	1/1/00 11:00 a.m.	1/31/00 9:00 a.m.	1/31/00 9:00 a.m.	1/15/2000 6:00 p.m.
Reliability level		95 %	90 %	95 %	97 %
Volatility level		5 %	10 %	5 %	10 %
Penalty per 1% reliability not meeting contract		$300	$25000	$20000	$12000

Figure 3. Customer demand and type of contract duration.

IV. SERVING CUSTOMER ENERGY SERVICES USING ENERGY CONTRACTS: AN ILLUSTRATION

In this section, an example is presented to illustrate the application of energy contracts in serving customers energy services. In this example, it is assumed that there are six periods in one day and one day in each month. Also, a company serving customers energy services has no load management capability and generation capacity. Six months of customer demand data and rate structure were shown in [4]. It will purchase all needed energy, including ancillary services, from an auction market, where the contract prices have been forecasted. There are 4 types of contracts, with each providing different reliability levels, volatility levels, and delivery duration. Table 2 shows the four types of contracts traded in the auction market and their specifications. The expected market prices for each of these contracts at different periods are shown in [4]. In this paper, the company is assumed to be price-taker, i.e., it has no control of market prices and pay according to market prices.

The scheduling customer demand algorithm used in this paper has been presented in [4]. The objective of the algorithm is to maximize the profit of serving customer energy services using the contracts purchased through the auction market. There are several sets of constraining equations. The first set of constraining equations requires the company to purchase sufficient energy to satisfy the contractual requirements with the customers. The second set of constraining equations requires that the customer demand volatility has met the volatility offered within the purchased contracts (Figure 2). These constraints consider the possibility that the company will not be compensated if customer demand falls below the volatility level. In addition, the customer demand will not be served if the demand exceeds the maximum volatility level offered by the contracts. The third set of constraining equations requires that the reliability levels desired by the customers were met at all time (Figure 1).

Using the scheduling customer demand algorithm, Table 3 shows the company’s performance from serving the customers energy services. In this example, due to its cost effectiveness, only contract type C is purchased by the company. A total of 1970.53 contracts type C are purchased during the six-month duration to serve 1950 MW-period of customer demand. 20.53 MW-periods are purchased to satisfy the reliability level requested by the customers. Since the contract type C has delivery duration of 1 period, the constraining equations on volatility has no impact on the amount of contracts that the company has to purchase.

Table 2. Specifications for contracts traded in an auction market.

Descriptions	Contract A	Contract B	Contract C	Contract D
Market	Forward	Forward	spot	Spot
Energy per contract	1 MW/period	1 MW/period	1 MW/period	1 MW/period
Reliability level,	95%	97%	95%	97%
Accepted volatility level,	5%	10%	5%	10%
Delivery duration	1 month	1 month	1 period	1 period
Beginning period	beginning of each month	Beginning of each month	Beginning of each period	Beginning of each period

Table 3. Company’s profitability in serving customers without the load management programs.

Description	Amount
Cost of energy	$131,989.61
Revenue	$186,820.00
Profit	$54,830.39

V. THE BENEFITS OF ADOPTING LOAD MANAGEMENT PROGRAMS

In section IV, in serving customer energy services, the company does not utilize any load management programs or owns generation capacity. In this section, the same company is assumed to have two groups of customers participating in the DLC program. The customers participating in the DLC programs are compensated based on the amount of energy deferred. Table 4 shows the contracts with the customers participating in the DLC program. Detailed controllable customer demand for the two types of customers during the 6 months duration is shown in [4]. The company does not have any ESS or any customer participating in the IDLC programs. In addition, the company has no generation capacity.

The scheduling customer demand algorithm that includes the constraining equations for DLC programs is shown in [4]. In addition to the constraining equations described in section IV, a fourth set of constraining equations limits the maximum deferrable demand at any period to the number of customer demand available for controllability.

In this example, due to its cost effectiveness, only contract type C is purchased by the company. A total of 1970.60 contracts type C are purchased during the six-month duration to serve 1950.07 MW-period of customer demand. 20.53 MW-periods are purchased to satisfy the reliability level requested by the customers. Additional 0.07 contracts, compared to 1970.53 contracts purchased in section IV, were purchased because the DLC program has increased the customer demand by 0.07 MW-period. Table 5 shows the company’s performance from serving the customers energy services. The cost of energy is reduced by $355.09. The cost savings has been passed on as improved profitability (by $115.66) to the company, as benefits to customers participating in the DLC program ($73.26), and as revenue loss (by $166.17) when the deferred energy is paid back and charged at a lower rate.

VI. THE IMPACT OF DIFFERENT CONTRACT SPECIFICATIONS

In section IV and V, due to their cost effectiveness, the company has purchased only contracts type C to serve the customers. In this section, the market price for contract type B at the 31^st period is lowered (assuming a new forecasted market price), from $78.81/MW-period to $58.81/MW-period, to observe the impact of purchasing contracts with different contract specifications.

With the new market price, 1665.17 contracts type C and 50.21 contracts type B are purchased to serve 1949.81 MW-period customer demand during the six months duration. 16.63 MW-periods are purchased to satisfy the reliability level requested by the customers. This amount of energy is much lower than the results found in the last two sections because contracts type B are offering higher reliability level. The customer demand, after scheduling the DLC demand, is 0.09 MW-period lower than the initial customer demand, which is 1950 MW-period. The adopted scheduling customer demand algorithm assumes that buying contracts offering higher reliability level has the same effect of buying additional energy contracts in ensuring that the customers’ requests are met.

Table 6 shows the company’s performance from serving the customers energy services. The improved profitability, $356.37, is higher than the energy cost saving, $355.19. This is because the market price is lower and the number of contracts purchased to ensure the desired reliability level is lower. In this example, the benefits to customers participating in the DLC program is higher because different scheduling sequences for DLC demand is adopted in this section.

levels.

Table 4. Contract with DLC program’s customers.

Item		Customer type
		1	2
Size of customers participating		24	24
Min/Max control duration		1/2	1/2
Max control duration(periods)		8 1/3	8 1/3
Reliability of the DLC devices		97%	97%
Rebate scheme	Fixed part	$0.00/month	$0.00/month
	Variable part	$2.50/MW deferred	$2.50/MW deferred
Pay back ratio for 1 period of control after energy deferment	1^st period	40% of total deferred	35% of total deferred
	2^nd period	55% of total deferred	40% of total deferred
	3^rd period	0% of total deferred	25% of total deferred
Pay back ratio for 2 periods of control after energy deferment	1^st period	30% of total deferred	25% of total deferred
	2^nd period	25% of total deferred	25% of total deferred
	3^rd period	25% of total deferred	25% of total deferred
	4^th period	25% of total deferred	20% of total deferred

Table 5. Company’s profitability in serving customers with the load management programs.

Description	Amount
Cost of energy	$131,634.42
Revenue	$186,653.73
Profit	$54,946.05
Benefits to customers participating in the DLC program	$73.26
Revenue loss caused by load management programs	$166.17
Improved profitability to the company	$115.66

Table 6. Company’s profitability in serving customers with the load management programs.

Description	Amount
Cost of energy	$128,131.55
Revenue	$186,605.92
Profit	$58,394.13
Benefits to customers participating in the DLC program	$115.66
Improved profitability to the company	$356.37

VII. CONCLUSIONS

This paper describes the possibility of serving customer energy services using energy contracts and load management programs. Based on an assumed auction market, this paper has shown that load management programs can be used, in conjunction to energy contracts, to improve the profitability of serving customer energy services. In addition, the results suggest that purchasing additional amount of energy contracts offering lower reliability levels has the same effect of purchasing fewer energy contracts offering higher reliability levels.

VIII. REFERENCES

[1] G. B. Sheblé, Computational Auction Mechanisms for Restructured Power Industry Operation. Boston: Kluwer Academic Publishers, 1999.

[2] S. Dekrajangpetch, “Auction Development for the Price-based Electric Power Industry,” Doctoral dissertation, Iowa State University, Ames, December, 1999.

[3] J. Kumar, “Electric Power Auction Market Implementation and Simulation,” Doctoral dissertation, Iowa State University, Ames, December, 1996.

[4] K. H. Ng, “Operational Planning of an Energy Service Company,” Doctoral dissertation, Iowa State University, Ames, December, 2000.

[5] J. Skeer, “Highlights of the International Energy Agency Conference on Advanced Technologies for Electric Demand-òside Management,” in Proceeding of Advanced Technologies for Electric Demand-side Management, Sorrento, Italy: International Energy Agency, 1991.

[6] C. W. Gellings, and J. H. Chamberlin, Demand-side Management: Concepts and Methods. Lilburn, Georgia: Fairmont Press, Inc., 1993.

[7] K. –H. Ng, Reformulating Load Management under Deregulation, Master’s thesis, Iowa State University, Ames, May 1997.

[8] C. N. Kurucz, D. Brandt, S. Sim, “A Linear Programming Model for Reducing System Peak through Customer Load Control Programs,” presented at the IEEE PES winter meeting, 96 WM 239-9 PWRS, Baltimore, Maryland, 1996.

[9] B. Daryanian, R. E. Bohn, and R. D. Tabors, “Optimal Demand-Side Response to Electricity Spot Prices for Storage-Type Customers,” IEEE Trans. on Power Systems, Vol. 4, No. 3, August 1989.

[10] S. H. Lee, and C. L. Wilkins, “A Practical Approach to Appliance Load Control Analysis: A Water Heater Case Study,” IEEE Trans. on Power Apparatus and Systems, Vol. PAS-102, April 1983

[11] K. –H. Ng, and G. B. Sheblé, “ Direct Load Control - A Profit-Based Load Management using Linear Programming,” IEEE Trans. Power Systems, Vol. 13, No. 2, May 1998.

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IX. BIOGRAPHIES

Kah-Hoe Ng received his BSEE with distinction (May, 1995), MSEE (May, 1997), MSECON (December, 2000), and PhD (December, 2000) from Iowa State University (ISU). His research interests include demand side management, power economics and optimization.

Gerald B. Sheblé (M 71, SM 85) is a Professor of Electrical Engineering, ISU, Ames, Iowa. Dr. Sheblé received his BSEE and MSEE degrees from Purdue University and his Ph.D. in EE from Virginia Tech. His industrial experience includes over fifteen years with a public utility, a research and development firm, a computer vendor, and a consulting firm.