BIDDING STRATEGY OF VIRTUAL POWER PLANT IN ELECTRICITY AND CARBON EMISSIONS TRADING MARKETS
Date
2026
Authors
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Publisher
Department of Electrical Engineering, NITS
Abstract
The growing adoption of renewable energy and the shift toward decentralized power
generation have driven the development of Virtual Power Plants (VPPs), which serve as
an effective approach to combine various Distributed Energy Resources (DERs) into a
single, adaptable, and market-oriented system. This thesis presents a comprehensive study
aimed at enhancing the operational efficiency, economic viability, environmental
sustainability, and cyber security of VPPs through five interrelated objectives.
First, a forecasting model for renewable energy resources is designed to
precisely estimate the generation from variable sources like solar and wind. Leveraging
advanced machine learning methods, this model enhances the reliability and scheduling
efficiency of VPPs, enabling more stable grid integration and improved participation in
energy markets.
Second, recognizing the volatile nature of carbon trading markets, a Carbon
Price Forecasting Framework is proposed. This model incorporates market uncertainties
and utilizes hybrid forecasting techniques, including deep learning, time series
decomposition, and feature selection methods, to provide precise carbon price predictions.
Accurate forecasting supports VPPs in making informed trading and operational decisions
while complying with carbon regulations.
Third, social welfare oriented bidding strategy is developed to guide VPPs in
participating in both energy and carbon markets, aiming to balance profit maximization
with broader social and environmental obligations. The forecasted results for solar PV
and wind power from chapter 2 have been taken for participating in VPP trading. Various
carbon emission reduction methods are presented namely: carbon tax, cap-and-trade
scheme and uplift payment scheme. The carbon credit price is calculated using carbon tax
and uplift payment scheme and then participated in EM. For a day ahead bidding strategy
considers carbon trading constraints, emission penalties, and renewable integration while
aiming to enhance overall welfare, particularly in systems with high renewable
penetration.
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After considering bidding strategy, the thesis conducts a thorough analysis on
Unit Commitment (UC) for VPP systems. Therefore, fourth objective this thesis, bi-level
UC model for VPP has been presented. For UC model the VPP is again integrated with
carbon trading market (CTM) and also with green certificate trading market (GCTM).
The proposed bi-level unit commitment model aims to maximize the total profit of the
overall VPP systems by minimizing the total cost. By integrating EM with CTM and
GCTM, the model optimizes energy dispatch, minimizes emissions, and supports
regulatory compliance. The analysis also evaluates the economic implications of carbon
prices on dispatch decisions and generation cost.
Since, the concepts of VPP is ICT based structure and therefore the
maintaining its security is foremost challenge for a power engineer. Finally, fifth
objective of the thesis explores the Cyber security aspects of VPPs, recognizing the
critical importance of secure data communication and control in a digitally connected
energy ecosystem. This also presents the possible cyber-attacks in in VPP systems and its
impacts. Potential threats and vulnerabilities are identified, and a cyber-security
framework is proposed, including anomaly detection, secure protocols, and resilience
strategies to safeguard VPP operations against cyber-attacks. The proposed algorithm
Autoencoder utilized for anomaly detection for 9-bus and IEEE-39 bus modified VPP
systems created in MATLAB Simulink.
The outcomes of this research offer meaningful insights and practical
approaches to enhance the technical efficiency, economic viability, and environmental
sustainability of VPPs. By addressing forecasting accuracy, carbon market integration,
optimal bidding, emission reduction, and cyber security, the thesis contributes to the
advancement of future-ready, low-carbon, and resilient power systems.