Demand Response and FERC Mandated Compensation Issues

2012 ◽  
Author(s):  
Donald Lincoln
Keyword(s):  
Energy Efficiency ◽  
Demand Response ◽  
Market Competition ◽  
Peak Load ◽  
Market Price ◽  
Energy Markets ◽  
Local Market ◽  
Wholesale Market ◽  
Market Prices ◽  
Demand Response Programs

This paper describes a Demand Response (DR) pilot event performed at Sandia National Laboratories in August of 2011. This paper includes a description of the planning for the demand response event, sources of energy reduction during the event, the potential financial benefit to Sandia National Laboratories from the event, event implementation issues, and the event results. In addition, this paper presents the implications of the Federal Energy Regulatory Commission (FERC) Order 745, Demand Response Compensation in Organized Wholesale Energy Markets, issued in March 2011. In this order FERC mandates that demand response suppliers must be compensated by the organized wholesale energy markets at the local market price for electricity during the hour the demand response is performed. Energy management in a commercial facility can be segregated into energy efficiency and demand response. Energy efficiency focuses on steady state load minimization. Demand response reduces load for event-driven periods during the peak load. Commercial facility demand response refers to voluntary actions by customers that change their consumption of electric power in response to price signals, incentives, or directions from grid operators at times of high wholesale market prices or when electric system reliability is jeopardized. Demand-response-driven changes in electricity use are designed to be short-term and centered on critical hours during the day when demand is high or when the electricity supplier’s reserve margins are low. Demand response events are typically scheduled between 12:00 p.m. and 7:00 p.m. on eight to 15 days during the hottest period of the year. Analysis has determined that automated demand response programs are more efficient and effective than manually controlled demand response programs due to persistence. FERC has stated that their Order 745 ensures organized wholesale energy market competition and removes barriers to the participation of demand response resources. In Order 745, FERC also directed that the demand response compensation costs be allocated among those customers who benefit from the lower prices for energy resulting from the demand response. FERC has allowed the organized wholesale energy markets to establish details for implementation methods for demand response compensation over the next four years following the final Order issue date. This compensation to suppliers of demand response can be significant since demand response is typically performed during those hours when the wholesale market prices are at their highest levels during the year.

scholarly journals Welfare Maximization-Based Distributed Demand Response for Islanded Multi-Microgrid Networks Using Diffusion Strategy

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3701 ◽  
Author(s):  
Haesum Ali ◽  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Jinhong Jeon ◽  
Hak-Man Kim
Keyword(s):  
Demand Response ◽  
Peak Load ◽  
Market Price ◽  
Consensus Method ◽  
Welfare Maximization ◽  
Total Surplus ◽  
Load Demand ◽  
Price Signals ◽  
Demand Response Program ◽  
Demand Response Programs

Integration of demand response programs in microgrids can be beneficial for both the microgrid owners and the consumers. The demand response programs are generally triggered by market price signals to reduce the peak load demand. However, during islanded mode, due to the absence of connection with the utility grid, the market price signals are not available. Therefore, in this study, we have proposed a distributed demand response program for an islanded multi-microgrid network, which is not triggered by market price signals. The proposed distributed demand response program is based on welfare maximization of the network. Based on the welfare function of individual microgrids, the optimal power is allocated to the microgrids of the network in two steps. In the first step, the total surplus power and shortage power of the network is determined in a distributed way by using the local surplus/shortage information of each microgrid, which is computed after local optimization. In the second step, the total surplus of the network is allocated to the microgrids having shortage power based on their welfare functions. Finally, the allocated power amount and the initial shortage amount in the microgrid is used to determine the amount of load to be curtailed. Diffusion strategy is used in both the first and the second steps and the performance of the proposed method is compared with the widely used consensus method. Simulation results have proved the effectiveness of the proposed method for realizing distributed demand response for islanded microgrid networks.

scholarly journals Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yuling Li ◽  
Xiaoying Wang ◽  
Peicong Luo
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Smart Grids ◽  
Peak Load ◽  
Two Stage ◽  
The Sustainable Development ◽  
Second Stage ◽  
Potential Demand ◽  
Electricity Costs ◽  
Demand Response Programs

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

THE LEVEL AND PRICE VOLATILITY OF BLUEBERRY FRUITS (VACCINIUM CORYMBOSUM L.) OBTAINED BY PRODUCER AND ON THE WHOLESALE MARKET DURING 2007-2016

2017 ◽  
Vol XIX (2) ◽  
pp. 275-281 ◽  
Author(s):  
Wioletta Wróblewska ◽  
Eugenia Czernyszewicz
Keyword(s):  
Export Price ◽  
Market Price ◽  
Distribution Channel ◽  
Price Volatility ◽  
Vaccinium Corymbosum ◽  
Domestic Market ◽  
Wholesale Market ◽  
Price Changes ◽  
Market Prices

The aim of the study was to assess the level and volatility of prices of blueberry obtained in the farm (in wholesale on the domestic market and in export) and on the wholesale market during 2007-2016, due to choice of distribution channel. The level, direction and intensification of price changes were analyzed. The study shows that the prices of blueberry at the producer level were characterized by greater volatility than the wholesale market. Prices obtained by the producers on wholesale on the domestic market were significantly lower than in exports and in the wholesale market, on average in the analyzed period accounted for only 69% of the export price and 52% of the wholesale market price. Regardless of where the price comes from,the highest price for fruits was obtained in September, and the lowest in August, which is the month of the largest supply of fruits on the market.

scholarly journals Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3361 ◽  
Author(s):  
Venkat Durvasulu ◽  
Timothy Hansen
Keyword(s):  
Demand Response ◽  
Power Market ◽  
Electric Utility ◽  
Energy Markets ◽  
Power Markets ◽  
High Price ◽  
Test Case ◽  
Market Prices ◽  
Bulk Power

In most U.S. market sponsored demand response (DR) programs, revenue earned from energy markets has been relatively low compared to DR used for capacity markets and ancillary services. This paper presents an aggregated DR model participating in the bulk-power market as a service through a pool-based entity called demand response exchange (DRX). Using the DRX structure, DR providers can participate in energy markets as a service to benefit bulk-power market entities. The benefits and challenges to each market entity using DR-as-a-service are presented in an extended review. The DRX model in this study is a market entity that operates with the day-ahead market to select DR offers that minimize electric utility payments. A case study was performed using the proposed DRX model on the IEEE 24-bus system, augmented to represent actual bulk-power market prices to study factors that influence utility payments under the DRX-market paradigm. Two high-price days of the PJM market were simulated, and it was shown for a single day on the augmented test case that spending $69,955 for DR-as-a-service results in a reduction of utility payments of $864,199. The day-ahead generator supply curve, network congestion, and DR curtailment were found to be the most influencing factors that impact the benefit of using DR-as-a-service.

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