Combinatorial double auctions for multiple microgrid trading


This paper presents an auction mechanism for energy trading between multiple microgrids. We consider a region consisting of multiple interconnected microgrids wherein given time t, some microgrids have excessive energy that they wish to sell, whereas other microgrids desire to buy additional energy to meet their local demands. In this paper, we introduce a combinatorial double auction mechanism for such trades to happen. The participants can bid as a combination of bids along with a single bid format. We also present a novel winner determination solution for combinatorial double auctions using evolutionary algorithms. Two algorithms combining genetic algorithm and particle swarm optimization are presented in this paper. Price determination for each trade is also explained. Using MATLAB, performance evaluation and stability tests of the proposed auction technique are performed and presented.

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This work was supported in parts by NRF-2016K2A9A2A11938310 (Korea–China Project) and the Gyeonggi Regional Research Center (GRRC) program of Gyeonggi Province under Grant GRRC Hanyang 2016-B01 (IoT/CPS-based Factory Energy Management System).

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Correspondence to Seung Ho Hong.


Appendix A: Energy Forecasting

The energy forecasting system is the term used to describe the forecasting of load demands, expected energy production, and, in some cases, prices. Generally, energy forecast systems can be divided into two types: load and generation, as described below.

A.1 Load forecast systems

The main task of load forecast systems is calculation of the expected load over the course of the specified time period. Load forecast systems can be divided into three types: (a) short-term, (b) mid-term, and (c) long-term load forecast systems. Short-term load forecast systems are used for predicting the amount of load from 1 h to several hours. Mid-term systems are used for forecasting the load up to a month. Long-term forecasting systems are used for large future predictions, forecasting the load up to several months to years ahead [39]. Based upon the results of mathematical models, the load forecast systems can be divided into two further types: linear models and nonlinear models. Linear models are based on synthesizing all features of the problem to be solved in complex equations. Studies using linear models have long since been overtaken by nonlinear models. One of the main reasons for this is the nonlinearity of demand prediction. Nonlinear models can also be used to model the relationship between periodical and residual components accurately [39]. In this study, we consider that microgrids are equipped with short-term load forecast systems. Moreover, microgrids are considered as nonlinear loads [30], so nonlinear forecast systems are used for this purpose. Nonlinear energy forecast models are solved using artificial intelligence systems mostly based on neural networks [40,41,42,43].

A.2 Generation forecast systems

The main task of generation forecast systems is calculation of the expected energy generation over the course of a specified time period. Generation forecast systems are mostly considered based on their application or method of generation. In our case, wind and solar are the two methods of generation; hence, wind and solar generation forecast systems are required. As per the timescale, generation forecast systems can be divided into short-term, mid-term, and long-term forecast systems. Short-term systems are used for electricity market clearing and real-time grid operation; mid-terms are used for operational security in the electricity market and reserve requirement decisions. Long-term systems are used for maintenance planning and operation management. In terms of modeling, generation systems can be divided into physical methods and statistical methods. Physical methods use numerical weather prediction methods, such as wind speed, temperature, daylight data. These methods are considered best for long-term planning. Statistical methods are used for complex and nonlinear systems. They use a combination of regression methods with learning methods from previous data and weather predictions [44,45,46]. In this study, bearing in mind the complexity of microgrids and electricity markets, we considered the participants to be equipped with short-term generation forecast systems based on a statistical model. For solving the statistical model, artificial neural networks stand out as one of the best methods [46].

Appendix B

Day-Ahead Market (DAM)

DAMs are usually organized as implicit auction systems where participants submit their bids. Figure 12 shows the working principle of DAMs. A bid should include at least information about energy quantity (named volume, kWh), min/max energy price (Euro/kWh) of sale/purchase, and the reference delivery period of the day. After market closure, all presented bids for sale and purchase are arranged together, in accordance with a specific merit-order criterion to build the aggregated curves of supply and demand. The clearing price and clearing volume are obtained by the intersection of the two curves. The clearing price corresponds to the offer price of the most expensive bid accepted for supply; it is the price at which the energy provided by all of the agents in a specific system or bidding zone is remunerated [19].

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Zaidi, B.H., Hong, S.H. Combinatorial double auctions for multiple microgrid trading. Electr Eng 100, 1069–1083 (2018).

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  • Combinatorial double auction
  • Electricity market
  • Energy trading
  • Evolutionary algorithms
  • Microgrid
  • Smart grid