Study of AMES Wholesale Power Market Testbed

Size: px

Start display at page:

Download "Study of AMES Wholesale Power Market Testbed"

Christal Wiggins
5 years ago
Views:

Study of AMES Wholesale Power Market Testbed Priyanka S Kole 1 1 PG Scholar, EEE Dept,SDMCET,Dharwad Abstract This study reviews the AMES wholesale power market test bed features and analysis of

1 Study of AMES Wholesale Power Market Testbed Priyanka S Kole 1 1 PG Scholar, EEE Dept,SDMCET,Dharwad Abstract This study reviews the AMES wholesale power market test bed features and analysis of double auction pool market with a simple two bus system. Keywords AMES, ISO, LSE, LMP, GenCo, DC-OPJ I. INTRODUCTION AMES is a free open source computational laboratory for the Agent-based Modeling of Electricity Systems. The wholesale power market design proposed by the U.S Federal Energy Regulatory Commission (FERC)in April 2003 white paper by Leigh Tesfatsion Professor of Economics, Mathematics, and Electrical and Computer Engineering Iowa State University, Ames, IA A. Overview of AMES Key Features The AMES(V2.05) wholesale power market operates over an AC transmission grid starting with hour 00 of day and continuing through hour 23 of a user-specified maximum day. AMES includes an Independent System Operator (ISO) and a collection of energy traders consisting of J Load-Serving Entities (LSEs) and Generation Companies (GenCos) distributed across the buses of the transmission grid. The objective of the not-for-profit ISO is the maximization of Total Net Surplus (TNS) subject to transmission constraints and GenCo operating capacity limits. In an attempt to attain this objective, the ISO operates a day-ahead energy market settled by means of LMP.The benefit of each LSE j is measured by the net earnings it achieves through the purchase of power in the day ahead market and the resale of this power to its customers. During the morning of each day D, each LSE j reports a demand bid to the ISO for the day-ahead market for day D+1. Each demand bid consists of two parts: fixed demand (i.e., a 24-hour load profile) to be sold at a regulated price r to its customers with fixed-price contracts; and 24 price-sensitive inverse demand functions, one for each hour, reflecting the price-sensitive demand of its customers with changing-price contracts.the objective of each GenCo i is to secure for itself the highest possible net earnings each day through the sale of power in the day ahead market. During the morning of each day D, each GenCo i uses its current choices based on probability to choose supply offer from its action domain ADi to report to the ISO for use in all 24 hours of the day ahead market for day D+1. Each supply offer in ADi consists of a linear marginal cost function defined over an operating capacity interval..genco i s ability to vary its choice of a supply offer from ADi permits it to adjust the slope of its reported marginal cost function and the upper limit of its reported operating capacity interval in order to increase its daily net earnings. After receiving demand bids from LSEs and supply offers from GenCos during the morning of day D, the ISO determines and posts hourly bus LMP levels as well as LSE cleared demands and GenCo dispatch levels for the day-ahead market for day D+1. These hourly outcomes are determined via Security-Constrained Economic Dispatch (SCED) formulated as bid/offer-based DC Optimal Power Flow (DC-OPF) problems with approximated TNS objective functions based on reported rather than true GenCo costs.at the end of each day D the ISO settles the day-ahead market for day D+1 by receiving all purchase payments from LSEs and making all sale payments to GenCos based on the LMPs for the day-ahead market for day D+1, collecting difference (if any) as ISO net surplus.the ISO determines hourly power supply commitments and LMPs for the day ahead market by All Rights Reserved 66

2 hourly bid/offer based DC optimal power flow (DCOPF) problems that approximate Underlying ACOPF problems. The ISO solves its DCOPF problems by calling an accurate and efficient DCOPF solver, DCOPFJ, incorporated into AMES. This permits LSEs to submit price sensitive as well as fixed demands to the ISO for the day ahead market.iso is concerned about loss of operational efficiency due to GenCos Market Power. That is A GenCo can increase its net earnings either by reporting a higher than true marginal cost function or by reporting a less than true upper operating capacity limit. Hence, as an approach to GenCo market power mitigation, the ISO can impose asupply offer price cap(pcap).at this case, GenCo cannot exceed PCap. The user can control the length of each run(simulation) by choosing following stopping rules: Stop when specified maximum day is reached Stop when each GenCo is choosing a single offer with a probability that exceeds a user specified threshold probability. Stop when probability distribution used by each GenCo to select supply offers has stabilized to within a user specified threshold for specified number of days. Stop when supply offer selected by each GenCo has stabilized to within a user specified threshold for a user specified number of days. Stop when the net earnings of each GenCo has stabilized to within a user specified number of days. When multiple stopping rules are called for, the simulation run terminates as soon as any one of the rules is satisfied. B. DC-OPF Problem Formulation The standard hourly bid/offer-based DC optimal power flow (DC-OPF) problem formulation for an ISO-managed day ahead energy market involves the maximization on day D of reported TNS (Total Net Surplus) for a particular hour H of day D+1 subject to transmission and generation capacity constraints in approximate linear form. Total net surplus refers to the sum of LSE, GenCo, and ISO net surplus. The ISO must consider its total net surplus calculation on LSE demand bids and GenCo supply offers rather than on their true purchase and sale reservation values, which are not directly observable by the ISO. AMES (V2.05) solves this standard DCOPF problem via DCOPFJ, a highly accurate and efficient DC-OPF module. DC Optimal Power Flow Problem: max TNS (1) with respect to LSE real-power price-sensitive demands, GenCo real-power generation levels, and voltage angles. P S Lj ; j = 1; :::; J; P Gi; i = 1; :::; I; k,k = 1; :::;K (2) subject to 1. a real-power balance constraint for each bus k=1,...,k: 2. Alimit on real-power flow for each branch km: (3) (4) 3. a real-power operating capacity interval for each GenCo i = 1,...,I: All Rights Reserved 67

3 4. a real-power purchase capacity interval for price-sensitive demand for each LSE j = 1,...,J: 5. a voltage angle setting at angle reference bus 1 (6) Thus DC-OPF solver considers all the above constraints before solving any bidding strategies in a double auction pool market. Figure1 below shows activities of ISO considered in AMES. (7) Figure 1. AMES ISO activities during a typical day D. II. STUDY OF A TWO BUS SYSTEM A simple two bus system is considered which involves power market settlement between two GenCos and one LSE. It is an example of double auction pool market where, both Gencos and LSEs present their supply bids and buy bids to the ISO(Independent System Operator).The market is settled by ISO by offering a market clearing price which is obtained by intersection of highest supply bid with that of lowest buy bid along with load forecast curve. The same is explained with the figure 3. Figure 2. A Two Bus All Rights Reserved 68

4 Figure 3.Market settlement in Double Auction Power Pools The objective function of such a Double auction pool market is to achieve maximization of social welfare function thus obtain market price....(8) Where, Ci=Cost function of GenCo i expressed as function of power supplied Pi Bj=Benefit function of customer j as function of power demand PDj M=Number of Demand bids, N=Number of Supply bids Social welfare function, J is Subjected to an equality constraint given by...(9).(10) Where, BPB=Demand Bid Prices, BPS=Supply Bid Prices Now applying Langrangian we get objective function as: Now applying Kuhn-Tuckers condition of optimality, we get..(11) (12) Where, is Langrangian multiplier which denotes system marginal price or Locational Marginal Price(LMP). Fuel cost characteristics of the two GenCos is given below: F1=0.005P P F 2 =0.006P P Where P 1 and P 2 are Generation Company 1&2(GenCo1,GenCo2). LMP 1, LMP 2 are Locational Marginal Price at bus1 and bus2 All Rights Reserved 69

5 A. Results and Inferences: This section involves the input and output data obtained ( in the form of tables and graphs)for a double auction pool market settled buy a ISO. A.1 Input Case Parameters Table.1 GenCo1 Input Characteristics Table.2 GenCo2 Input Characterisitics Table.3 LSE Fixed Demand for One Day Figure.4 Genco1 s Input Characteristics Figure 5. Genco2 s Input All Rights Reserved 70

10 Figure 12.Market Settlement of Double auction pool Market at 17 th Hour of a Day A.3 Inferences: In a double auction pool market, the ISO carry out the market settlement by arranging supply offers by GenCos in increasing order and demand offers or bids by LSEs in decreasing order. Thus the market clearing price is obtained at the intersection of such bid curves with load forecast curve.this activity of ISO is limited only to markets involving less number of GenCos and LSEs.If more number of participants exist in a pool market then AMES Tool helps in settling such markets. AMES tool uses a DCOPF-J solver to obtain market clearing price as shown in figure 12.In this case increasing supply bid curves and decreasing demand bid curve intersects at 17 th hour of a day with market clearing price equal to 17.2 $/MWh. This marginal price correspond to an equal supply and bid price of $ at an equal supply and demand power of 493MW.The simulations are carried out for a user specified day length of 50 days and stopping rule one is considered in this study. III.CONCLUSION It can be inferred from the above results that the double auction pool market is settled by ISO defined LMP and the stopping rule one is called for the terminating of execution by DC-OPFJ solver. Thus, the proposed work here enhances the importance of AMES tool in wholesale power market settlement study. REFERNCES 1. Deddy Koesrindartoto, Junie Sun, and Leigh Tesfatsion (2005), "An Agent Based compu- -tational Laboratory for Testing the Economic Reliability of Wholesale Power Market Designs Proceedings of the IEEE Power and Energy Society GeneralMeeting, San Francisco, California, June 1216,pp Hongyan Li and Leigh Tesfatsion (2009), "Development of Open Source Software for Power Market Research: The AMES Test Bed" (pdf preprint,628kb), Journal of Energy Markets, Vol. 2, No. 2, All Rights Reserved 75