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BLOCKCHAIN FOR TRANSACTIVE ENERGY MARKET WITH NETWORKED MICROGRIDS
Title
BLOCKCHAIN FOR TRANSACTIVE ENERGY MARKET WITH NETWORKED MICROGRIDS
Creator
Yan, Mingyu
Date
2021
Description
Transactive energy, which allows and incentivizes microgrids (MGs) to trade energy with each other, is regarded as the next-generation energy...
Show moreTransactive energy, which allows and incentivizes microgrids (MGs) to trade energy with each other, is regarded as the next-generation energy management scheme to accommodate the penetration of distributed energy resources (DERs). Blockchain provides an effective and decentralized strategy, which can address the operational challenges introduced by the transactive energy market. This thesis is aimed at providing effective transactive energy markets for incentivizing MGs to trade energy and utilizing blockchain technologies to provide a secure and efficient energy trading environment for all participants.First, this thesis offers a centralized transactive market for networked MGs to transact energy through the centralized distribution system operator (DSO) while ensuring the power network limits. All MGs cooperate in this market and the cooperative behaviors are captured using the cooperative game with externalities. A two-level problem is studied to allocate the total payoff to all participating MGs. Numerical results for a 4-MG system and the IEEE 33-bus show the validity of the centralized transactive energy model. Second, this thesis proposes a two-level network-constrained peer-to-peer (P2P) transactive energy for multi-MGs, which guarantees the distribution power network security and allows MGs to trade energy with each other flexibly. At the lower level, a P2P transactive energy is employed for multi-MGs to trade energy with each other. A multi-leader multi-follower (MLMF) Stackelberg game approach is utilized to model the energy trading process among MGs. At the upper level, the DSO reconfigures the distribution network based on the P2P transactive energy trading results by applying the AC optimal power flow considering the distribution network reconfiguration. If there are any network violations, the DSO requests energy trading adjustments at the lower level for network security. Numerical results for a 4-MG system, the modified IEEE 33-bus, and the 123-bus distribution power systems show the effectiveness of the proposed transactive energy model and its solution technique. Third, this thesis adopts the blockchain for the peer-to-peer transactive energy market among MGs. A two-level integrated blockchain-power system is provided, in which all MGs and the DSO are equipped with blockchain. At the lower level, MGs trade energy with each other through the lower-level MG blockchain, while the DSO manages the network security through the upper level DSO blockchain. We illustrate how to utilize blockchain technologies, i.e., public and private keys and smart contracts, to provide an efficient and secure energy trading environment for all MGs. Last, this thesis applies the blockchain for transacting energy and carbon right for networked MGs. MGs transact energy and carbon right through the centralized DSO while ensuring the power network limits. The introduction of blockchain achieves secure and decentralized market settlements in this centralized market. Numerical results for a 4-MG system and modified IEEE 33-bus systems show the effectiveness of the proposed transactive energy and carbon market.
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Application of Blockchain and Artificial Intelligence Methods in Power System Operation and Control
Title
Application of Blockchain and Artificial Intelligence Methods in Power System Operation and Control
Creator
Farhoumandi, Matin
Date
2023
Description
The proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving...
Show moreThe proliferation of distributed energy resources (DERs) and the large-scale electrification of transportation infrastructure are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in a coordinated system of power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer-to-peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this thesis, we propose the applications of artificial intelligence technology to power system operation and control. First, blockchain (BC) is applied to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralized privacy-preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC’s smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN. Second, machine learning and deep learning methods are applied to short-term forecasting of non-conforming net load (STFNL). STFNL plays a vital role in enhancing the secure and efficient operation and control of power systems. However, power system consumption is affected by a variety of external factors and thus includes high levels of variations. These variations cause STFNL to be a challenging task as more DERs are integrated into the power grid. This thesis proposes two commonly used machine learning and deep learning methods, i.e., ensemble bagged and long short-term memory, for STFNL. The advantages, features and applications of these methods are expanded in a proposed fusion forecasting model that improves the STFNL accuracy. Additionally, data engineering and preprocessing options are used to increase the accuracy of the proposed fusion model. A comparative study based on practical load data is performed to demonstrate that the proposed fusion methodology can reach a relatively higher forecasting accuracy with lower error indices. Index Terms—Blockchain, deep learning and machine learning, electric vehicle charging stations, non-conforming net load forecasting, peer-to-peer transactive energy, power distribution and transportation networks, distributed energy resources, behind-the-meter supply resources.
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