Probabilistic evaluations on marginal price and capacity adequacy of power systems with price-elastic demand

Demand response (DR) is one of the major stakeholders in the smart grid and has been used as an energy reconciler between supply and demand. After a literature overview, the importance of the paper is enhanced by having a theoretical and behavioral-based analysis of DR in power systems. In this work, the potential factors that influence more DR among customers and the residential market as a whole have been discussed. The customers’ elastic demand approach can pave the way for adapting a responsive demand mechanism that ensures the system reliability and cost effective measures. Alternatively, this approach can make the program more effective and supportive in serving the social welfare as whole.

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Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme

Energies ◽

10.3390/en12071261 ◽

2019 ◽

Vol 12 (7) ◽

pp. 1261

Author(s):

Neda Hajibandeh ◽

Miadreza Shafie-khah ◽

Sobhan Badakhshan ◽

Jamshid Aghaei ◽

Sílvio Mariano ◽

...

Keyword(s):

Power Systems ◽

Wind Power ◽

Demand Response ◽

Electricity Markets ◽

Demand Curve ◽

Mixed Integer ◽

Programming Approach ◽

Elastic Demand ◽

Multi Objective ◽

System Operator

Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the high priority to the independent system operator (ISO). The energy and reserve markets clearing procedures have been run by using a multi-objective decision-making framework. In fact, its objective function includes the operation cost and the customer’s disutility based on the final individual load profile for each DR provider. A two-stage stochastic model is implemented to solve this scheduling problem, which is a mixed-integer linear programming approach. The presented approach is tested on a modified IEEE 24-bus system. The performance of the proposed model is successfully evaluated from economic, technical and wind power integration aspects from the ISO viewpoint.

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A Two-Stage Algorithm of Locational Marginal Price Calculation Subject to Carbon Emission Allowance

Energies ◽

10.3390/en13102510 ◽

2020 ◽

Vol 13 (10) ◽

pp. 2510 ◽

Cited By ~ 1

Author(s):

Mingxing Wu ◽

Zhilin Lu ◽

Qing Chen ◽

Tao Zhu ◽

En Lu ◽

...

Keyword(s):

Power Systems ◽

Electricity Markets ◽

Carbon Emission ◽

Optimal Solution ◽

Pareto Optimal Solution ◽

Pareto Optimal ◽

Emission Quota ◽

Quota Allocation ◽

Locational Marginal Price ◽

Marginal Price

To analyze the effect of carbon emission quota allocation on the locational marginal price (LMP) of day-ahead electricity markets, this paper proposes a two-stage algorithm. For the first stage of the algorithm, a multi-objective optimization model is established to simultaneously minimize the total costs and carbon emission costs of power systems. Hence, an evenly distributed Pareto optimal solution can be solved effectively by means of the normalized normal constraint method. For the second stage, a tracing model is built with the goal of minimizing the total costs of power systems and satisfying the constraints generated based on the Pareto optimal solution obtained from the first stage. Furthermore, the influence of carbon emission quota allocation on the LMP of electricity markets is analyzed, and different schemes to allocate carbon emission quotas are evaluated on a real 1560-bus and 52-unit system.

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Locational Marginal Price Calculation Implying Different System Uncertainties

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e4978.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 489-494

Keyword(s):

Power Systems ◽

Electrical Energy ◽

Operation Conditions ◽

Calculation Technique ◽

Locational Marginal Price ◽

Line Flow ◽

System Uncertainties ◽

Bus Networks ◽

The Cost ◽

Marginal Price

This paper presents locational marginal price (LMP) calculation technique facing different operation conditions. Uncertainties in power systems such as loosing transmission line of load change could be consider a common case. In calculating LMP, the effect of line flow limits will affect the prices at each bus. Also, the effect of shifted the load from bus to another is investigated. LMP is a technique used in market to determine the cost of supplying the next incremental electrical energy at a specific bus. Four different case of LMP are discussed in this paper. Three and four simple bus networks are used to demonstrate different cases of LMP.

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ALLOCATING THE VARIABLE COST OF TRANSMISSION LINES DUE TO ELASTIC LOADS IN A CONGESTED POWER MARKET

CONTEMPORARY ISSUES IN COMPUTING ◽

10.26480/cic.01.2020.99.102 ◽

2020 ◽

Author(s):

Srijan Banerjee ◽

Parnab Saha ◽

Bishaljit Paul ◽

Chandan Kumar Chanda

Keyword(s):

Transmission Lines ◽

Demand Function ◽

Quadratic Function ◽

Power Market ◽

Electrical Energy ◽

Variable Cost ◽

Elastic Demand ◽

Locational Marginal Price ◽

The Difference ◽

Marginal Price

In a competitive power market, the elastic demand for electrical energy transmission is viewed as a prime competitor of generator. Remote generators are needed for transmission to compete with local generators. The value of the transmission is based on the difference of Locational Marginal Price (LMP) of the generators across the network. To maintain the well operation of power market, LMPs which provide the price sensitivity is calculated at every bus. The revenue collected by the transmission owners is a convex quadratic function of the amount of power transmitted. This revenue provides a sound impact on investment perspective for setting the price that producers and customers should pay for the network. In this paper for a three bus system, the LMPs are calculated at the buses and a demand function for the transmission has been modeled which computes the maximum revenue for the optimal transmission capacity in the syste.

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Dual Deep Learning Networks Based Load Forecasting with Partial Real-Time Information and Its Application to System Marginal Price Prediction

Energies ◽

10.3390/en13010148 ◽

2019 ◽

Vol 13 (1) ◽

pp. 148 ◽

Cited By ~ 4

Author(s):

Khikmafaris Yudantaka ◽

Jung-Su Kim ◽

Hwachang Song

Keyword(s):

Power Systems ◽

Real Time ◽

Short Term Memory ◽

Time Interval ◽

Temperature Prediction ◽

Load Forecast ◽

The Real ◽

Load Power ◽

Prediction Time ◽

Marginal Price

Load power forecast is one of most important tasks in power systems operation and maintenance. Enhancing its accuracy can be helpful to power systems scheduling. This paper presents how to use partial real-time temperature information in forecasting load power, which is usually done using past load power and temperature data. The partial real-time temperature information means temperature information for only part of the entire prediction time interval. To this end, a long short-term memory (LSTM) network is trained using past temperature and load power data in order to forecast load power, where forecasted load power depends on the temperature prediction implicitly. Then, in order to deal with the case where nontrivial temperature prediction errors happen, a multi-layer perceptron (MLP) network is trained using the past data describing the relation between temperature variation and load power variation. Then, the temperature is measured at the beginning of the prediction time-interval and compensated load forecast is computed by adding the output of the LSTM and that of the MLP whose input is the temperature prediction error. It is shown that the proposed compensation using the real-time temperature information indeed improves performance of load power forecast. This improved load forecast is used to predict system marginal price (SMP). The proposed method is validated using the real temperature and load power data of South Korea.

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