The most important constraint of the power system i.e., supply-demand balance constraint can be met either by optimizing the generation side or the demand side. Demand response programs focus only on the consumption side of the network. But concentrating only on the consumption side might not fully exploit the capabilities of future smart grids. Thus, there is a need for a framework that focuses not only on the consumption side but also on the rate of generation in both grid and demand sides [1].
Traditionally the flow of energy is from wholesale power plants to transmission systems and then to the end-use consumers of the distribution system. The flow was uni-directional, but now with the increase in the percentage of variable and intermittent Distributed Energy Resources (DERs), the way the power system operates has changed [2]. Smart meters are the key component in today’s modern power grid as tracking of bidirectional power flows and data are now possible with them. Control of a large number of DERs can be done in a centralized or decentralized way. Decentralized way of trading like Peer-to-Peer (P2P) energy trading helps in lowering reserve requirements and reducing the losses in the power network [3]. In the P2P mechanism, the energy transactions take place without the need for a central controller.
Transactive Energy
Transactive Energy is defined as a set of economic and control mechanisms that allow the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter [4]. Decisions are made based on the price. Transactive control is the implementation of transactive energy. There is a bidirectional power flow due to the penetration of DERs such as Distributed Generation (DG), electric storage, and Electric Vehicles (EVs) in the distribution system.
The transactive energy framework helps the power system to evolve as a hybrid system of the Wholesale Energy Market (WEM) and Local Energy Markets (LEM). The wholesale energy market operator manages the network and power balance at the transmission level and the local energy market operator is responsible for managing the DERs and power balance at the distribution level. More accurate energy forecasting needs to be done in all the levels of power systems and by all the participants of the transactive market for successful implementation of the transactive energy framework.
Transactive Energy Management Framework
In a transactive energy management system, transactive agents negotiate their actions with each other through double auction transactive markets. A transactive agent may be a residential house, industrial or commercial building, microgrid, demand response aggregator, microgrid aggregator renewable farm, etc., As seen in figure 1, the transactive control concept can be applied in all levels of the power system from residential to transmission level and it facilitates the integration of local energy market and wholesale energy market.
At the bottom level of the transactive energy framework, we have residential houses, no matter their sizes, they can participate in the retail or local energy market and modify the schedule of its controllable appliances based on the cleared market price and user’s local information. The transactive energy concept can also be applied inside a single building where each zone can bid or compete among themselves. Retail markets can be operated for only one Microgrid (MG) as well as for the group of microgrids.
The retail market operator is responsible for balancing the electricity supply and demand at the distribution level. Transactive control helps in congestion and voltage management in the distribution system by considering the network operational constraints. At the transmission level, the WEM operator clears the market and determines the clearing price. Participants of the WEM are large loads, traditional bulk generation, grid-level renewable farms, and Distribution Companies (DISCOs). We have forward and spot transactions in transactive markets. Forward markets operate by relying on future delivery and spot market is used for instant delivery.
Energy Trading in Microgrid Transactive Energy Framework
Microgrid transactive energy framework can have centralized or decentralized energy trading. In a centralized energy trading, all microgrid participants send their energy bids and offers to the LEM operator. LEM operator aggregates all the bids and offers to determine the market-clearing price while satisfying the network constraints. The cleared market price is sent back to the participants to initiate the transactions.
Centralized energy trading uses cloud infrastructure for data and information exchange. The main disadvantage of centralized energy trading is scalability and risks of cyber-attacks. Decentralized energy trading uses distributed ledger technologies for the secure energy transaction process. A blockchain-based system can be used to implement decentralized P2P energy trading without involving any third party. Decentralized energy trading is more flexible and reliable.
Peer-to-Peer Trading
Recently electric distribution networks are experiencing tremendous changes with the proliferation of distributed energy resources, scilicet penetration of PV panels, battery energy sources, microturbines, etc. This trend leads to the drastic transition of market participants from consumers to prosumers. Like this peer-to-peer trading, a concept has emerged. (P2P) trading is a next-generation energy management technique for the smart grid that can enable prosumers to actively participate in the energy market either by selling their excess energy or buying deficient energy from the grid.
So, this P2P trading poses various challenges to the grid operator in technical and economic aspects since the existing infrastructure and market mechanism is no longer supported. Financial factors such as pricing mechanism, energy management, market operations. Technical elements like network and communication infrastructure. Literature has divided the P2P Market structure into two layers, named physical and virtual layers. The physical layer comprises grid connection, metering, and communication infrastructure. Pictorial representation of the P2P trading mechanism is shown in figure 2 [5].
The Virtual Layer
It consists of an information system, market operations, pricing mechanism, and energy
management system.
Information System
Being present in the virtual layer, it must provide some predefined set of rules to ensure the secure operation of the power system, which allows the market participants to communicate with another to share information in the energy trading process, provides equal access to all market participants, and integrate all market participants on some market platform in P2P trading. Smart contracts, a piece of code, are a promising solution deployed on the blockchain and provide the most secure trading platform. In addition, distributed ledger technology in the blockchain assures immutable financial transactions.
- Market Operation: Must provide some market-clearing procedure, specific bidding format. The pricing mechanism is one of the market operations that ensure the supply and demand balance. Therefore, it should also provide different time market horizons, such as one hour in the day ahead and 15 min in real-time markets.
- Pricing Mechanism: It is expected that the pricing mechanism ensures supply and demand balance by providing demand response and ancillary services. Consumers can reap more profits since they may get power for a price less than Feed-in Tariff (FIT) since the marginal cost of renewable energy is very low.
- Energy Management System: EMS participants can access energy in a real-time manner. So, a rational prosumer who has equipped with EMS buys power when the price is less than the marginal price.
- Physical Layer: Physical layer consists of information related to grid connection, metering, communication infrastructure.
- Grid Connection: P2P trading mechanism can be done in intra-microgrid and interconnected microgrids, sell the excess energy to the grid, and buy deficient energy from the grid. So, a microgrid should consist of effective interconnection points. Therefore, the cost of purchasing energy is more petite at these interconnections and selling energy is more, and losses in a microgrid are less.
- Metering: In this trading mechanism, prosumers should be equipped with traditional and transactive energy meters such that each prosumer can participate in the P2P trading process. So, the transactive energy meter makes the participants capable of reading the energy depends on the market conditions such as generation, demand, price, network conditions.
- Communication Infrastructure: A proper communication infrastructure must be provided by the P2P trading mechanism to consider communication latencies, throughput, reliability and security.
Energy Forecasting Using Smart Metering
Smart metering plays an important role in the transactive energy management process. Smart meters provide near real-time information related to energy consumption and its cost to both sellers and buyers. Based on the real-time energy consumption cost, consumers can forecast their energy consumption such that energy purchasing cost is minimized. Statistical techniques or artificial intelligence techniques can be used to forecast the load. Smart meters measure the consumption in minutes’ granularity and provided better solutions to the consumers so that they can choose customized plans for electricity consumption and curtailment. Also, smart meters can share information with devices at home and with other smart meters using various communication protocols.
So, optimization of energy in smart homes can be achieved using smart meters in transactive energy management. Clustering is the basic tool used to segregate the electricity customers belonging to the same consumption levels. Segmentation helps not only in competitive energy trading but also in inter and intra-regional energy exchange. Various clustering techniques like hierarchical clustering, K-means, and fuzzy K-means are generally used to segregate customers with similar consumption behaviour. Figure 3 and Figure 4 show the hourly clustering and total demand of each cluster done by using the k-means clustering method on data collected from a group of consumers [6].
Energy Management and Trading in Indian Context
Indian Energy Exchange Limited (IEX) and Power Exchange India Limited (PXIL) are the two power exchanges facilitating short-term trade of power in India. Power trading in India takes place through long-term power purchase agreements, short-term bilateral contracts, and exchange. In a short-term energy exchange, there are day-ahead markets and real-time markets. The market-clearing price and the quantity of electricity are determined through the closed double-sided auction mechanism.
Power from renewable sources can be traded through Renewable Energy Certificate (REC) market and one REC is equal to 1 MWh. Congestion between the bid areas is handled through the market splitting technique and area clearing price will be determined specific to the area.
Conclusion
Transactive energy can be considered as a framework that deals with the economic issues in energy trading and technical issues in the network operations. Transaction decisions are made based on the price or the value that is capable of being monetized. Transactive control can be implemented using a one-time information exchange approach and also an iterative information exchange approach.
In centralized energy trading, the central controller takes care of all the operations but the main challenge in implementing decentralized trading is to find the trusty participants and the way of exchanging money between them.
Energy Forecasting using smart meters described above will help in energy trading using virtual power plants as peer-to-peer transactions. The transactive grid will be a reality in the near future, which will help prosumers/aggregators to participate in the energy market. Thus, with the transactive energy framework, today’s power grid can be transformed into a more reliable and sustainable smart power grid.
References
- Omid Abrishambaf, Fernando Lezama, Pedro Faria, Zita Vale, “Towards transactive energy systems: An analysis on current trends”, in Energy Strategy Reviews, Volume 26,2019,100418, ISSN 2211-467X, https://doi.org/10.1016/j.esr.2019.100418.
- Hu, G. Yang, K. Kok, Y. Xue and H. W. Bindner, “Transactive control: a framework for operating power systems characterized by high penetration of distributed energy resources,” in Journal of Modern Power Systems and Clean Energy, vol. 5, no. 3, pp. 451-464, May 2017.
- Siano, G. De Marco, A. Rolán and V. Loia, “A Survey and Evaluation of the Potentials of Distributed Ledger Technology for Peer-to-Peer Transactive Energy Exchanges in Local Energy Markets,” in IEEE Systems Journal, vol. 13, no. 3, pp. 3454-3466, Sept. 2019, doi: 10.1109/JSYST.2019.2903172.
4. GridWise transactive energy framework, Version 1.0. PNNL- 22946 Ver1.0. The GridWise Architecture Council, Richland,2015
5. Tushar, T. K. Saha, C. Yuen, D. Smith and H. V. Poor, “Peer-to-Peer Trading in Electricity Networks: An Overview,” in IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3185-3200, July 2020, doi: 10.1109/TSG.2020.2969657.
6. http://www.thamesvalleyvision.co.uk/project-library/research-data/
Vidyamani T is a research scholar in the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, INDIA. Her areas of research are Energy Economics, Energy trading, and Microgrids.
Ampolu Maneesha is a research scholar in the Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, INDIA. Her areas of research are Restructuring Electricity Markets, P2P energy trading, and Optimization.
K Shanti Swarup is a professor with the Department of Electrical Engineering, Indian Institute of Technology Madras. His areas of research are Restructuring, Power System Economics, Electricity Markets, Operation, Optimization, Pricing, Forecasting, and Planning .
Does it have any relevance within the industry? Can this technic be utilised to optimise the overall consumption?