Abstract:In this paper, we propose novel techniques to reduce total cost and peak load of factories from a customer point of view. We control energy storage system (ESS) to minimize the total electricity bill under the Korea commercial and industrial (KCI) tariff, which both considers peak load and time of use (ToU). Under the KCI tariff, the average peak load, which is the maximum among all average power consumptions measured every 15 min for the past 12 months, determines the monthly base cost, and thus peak load control is extremely critical. We aim to leverage ESS for both peak load reduction based on load prediction as well as energy arbitrage exploiting ToU. However, load prediction inevitably has uncertainty, which makes ESS operation challenging with KCI tariff. To tackle it, we apply robust optimization to minimize risk in a real environment. Our approach significantly reduces the peak load by 49.9% and the total cost by 10.8% compared to the case that does not consider load uncertainty. In doing this we also consider battery degradation cost and validate the practical use of the proposed techniques.
This paper proposes a joint demand response and energy trading for electric vehicles in an off-grid system. We consider isolated microgrid in a region where, at a given time, some renewable energy generators have superflous energy for sale or to keep in storage facilities, whereas some electric vehicles wish to buy additional energy to meet their deficiency. In our system model, broker lead the market by determining the optimal transaction price by considering a trade-off between commission revenue and power reliability. Buyers and sellers follow the broker's decision by independently submitting a transaction price to the broker. Correspondingly transaction energy is allocated to the buyers in the proportion to their payment, whereas the revenue is allocated to the sellers in proportion to their sales. We investigated the economic benefits of such a joint demand response and energy trading by analyzing its hierarchical decision-making scheme as a single-leader-heterogeneous multi-follower Stackelberg game. With demonstrating an existence of a unique Stackelberg equilibrium, we show that the transaction price in the proposed market model is up to 25.8% cheaper than the existing power market. In addition, we compare the power reliability results with other algorithm to show the suitability of proposed algorithm in the isolated microgrid environment.
In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum’s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained.
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