Security Constrained Two-Stage Model for CO2 Emission Reduction

This paper introduces an innovative method for implementing demand response (DR) to enable household appliance scheduling to minimize CO2 emissions and improve the voltage security in transmission networks. A new demand response (DR) based on the time-varying emission curve is proposed in this paper to reduce CO2 emissions. In addition to emission-based DRs, non-responsive loads are considered. On the other hand, load modeling is believed to be one of the significant parts of the power system studies so that inaccurate load models can lead to dramatically incorrect simulation outputs leading to an unfortunate event such as the 1983 Swedish blackout. DR is therefore applicable to a number of loads, including induction type motors as well as exponential loads. In addition, both active and reactive DRs are considered in this model. This paper introduces a new model called the Security Constraint Two-Stage Framework arising from the complexity of the problem. This model includes a large scale (LS) stage and a small scale (SS) stage set in which the SS stage uses the LS stage results as inputs. The proposed design is being implemented on the IEEE 300 bus power network to investigate the desired objectives.

[1]  Xiao Chen,et al.  Response-Surface-Based Bayesian Inference for Power System Dynamic Parameter Estimation , 2019, IEEE Transactions on Smart Grid.

[2]  Mousa Marzband,et al.  Dynamic Carbon-Constrained EPEC Model for Strategic Generation Investment Incentives with the Aim of Reducing CO2 Emissions , 2019 .

[3]  Masood Parvania,et al.  Stochastic Unit Commitment in the Presence of Demand Response Program under Uncertainties , 2017 .

[4]  Koji Yamashita,et al.  Modelling and aggregation of loads in flexible power networks , 2014 .

[5]  Marek Reformat,et al.  Non-linear load modeling for simulations in time domain , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[6]  Thillainathan Logenthiran,et al.  Demand Side Management in Smart Grid Using Heuristic Optimization , 2012, IEEE Transactions on Smart Grid.

[7]  Zechun Hu,et al.  Carbon Flow Tracing Method for Assessment of Demand Side Carbon Emissions Obligation , 2013, IEEE Transactions on Sustainable Energy.

[8]  Jovica V. Milanović,et al.  Recommended Parameter Values and Ranges of Most Frequently Used Static Load Models , 2018, IEEE Transactions on Power Systems.

[9]  Yinyu Ye,et al.  A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles , 2011, IEEE Transactions on Smart Grid.

[10]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[11]  Jamshid Aghaei,et al.  Critical peak pricing with load control demand response program in unit commitment problem , 2013 .

[12]  Yijun Xu,et al.  Dynamic behavior of multi-carrier energy market in view of investment incentives , 2019, Electrical Engineering.

[13]  Kerry D. McBee,et al.  Applications of probability model to analyze the effects of electric vehicle chargers on distribution transformers , 2011, IEEE Transactions on Power Systems.

[14]  Aleksandar M. Stankovic,et al.  Parametric variations in dynamic models of induction machine clusters , 1997 .

[15]  F. D. Kanellos,et al.  Optimal Power Management With GHG Emissions Limitation in All-Electric Ship Power Systems Comprising Energy Storage Systems , 2014, IEEE Transactions on Power Systems.

[16]  Meisam Ansari,et al.  Transmission-service pricing by incorporating load following and correlation factors within a restructured environment , 2018, Electric Power Systems Research.