Recently, various renewable energy sources and large-scale batteries have been integrated into power grids, and renewable energy bidding and battery control become critical problems in the real-time energy market. However, bidding and control problems have been studied separately while these two problems simultaneously influence the total profit of renewable producers. In this paper, we propose a novel strategy where renewable energy bidding and battery control are collectively investigated. First, unlike the previous studies where bidding is simply the forecasted value, the proposed methods determine the bidding values considering the error compensability of the battery by switching the objective of forecasting from reducing errors to making errors compensable. After the error compensation, additional battery control is applied to utilize the energy arbitrage process considering the energy price. As there are energy price and renewable generation uncertainties, we propose a deep reinforcement learning based bidding combined with control, called DeepBid, for sequential decision making under uncertainty. Our extensive simulations with real solar and wind generation data show that the proposed DeepBid strategy substantially increases the total profit compared to existing bidding strategies by achieving as high revenues as the arbitrage strategy and as low deviation penalties as the error compensation strategy.

Increasing penetration of distributed renewable energy sources (DRESs) has resulted in the emergence of distributed energy resource aggregators (DERAs). A DERA participates in the transmission-level market operated by a transmission system operator (TSO), and the DERA's resources are connected to a jurisdiction of the distribution system operator (DSO). Inspired by the structure of the Korean power industry, this study assumes a minimal DSO that cannot directly dispatch the resources in its system. In this study, we develop a detailed procedure for prequalification wherein the DSO checks the DERA's bids that are submitted to the TSO markets. The proposed prequalification enables the DSO to secure the reliability of its system by providing limited network information to the DERA. The DERA modifies its bid until potential overvoltage and overflow problems are resolved, even in the worst case, including uncertainties. The proposed prequalification process is verified using the IEEE 33-bus distribution network. Compared to previous studies, the results demonstrate that the proposed prequalification can deal with distribution system constraints, even though uncertainties are included. The proposed prequalification process can be applied to power industries where the DSO does not have the full dispatch authority on DRESs.

Active Network Management (ANM) enables a microgrid to optimally dispatch the active/reactive power of its Renewable Distributed Generation (RDG) and Battery Energy Storage System (BESS) units in real time. Thus, a microgrid with high penetration of RDGs can handle their uncertainties and variabilities to achieve the stable operation using ANM. However, the actual power flow in the line connecting the main grid and microgrid may deviate significantly from the day-ahead bids if the bids are determined without consideration of the real-time adjustment through ANM, which will lead to a substantial imbalance cost. Therefore, this study proposes a formulation for obtaining an optimal bidding which reflects the change of power flow in the connecting line by real-time adjustment using ANM. The proposed formulation maximizes the expected profit of the microgrid considering various network and physical constraints. The effectiveness of the proposed bidding strategy is verified through the simulations with a 33-bus test microgrid. The simulation results show that the proposed bidding strategy improves the expected operating profit by reducing the imbalance cost to a greater degree compared to the basic bidding strategy without consideration of ANM.

This paper aims to empirically investigate consumers’ preference structures and their willingness to pay for key features of future residential electric power services that they have not experienced in the regulated market. Using a choice experiment based on conjoint survey and applying a mixed logit model, we quantitatively estimate individual utility functions for certain important attributes such as type of service-providing company, installation of smart meter devices, introduction of E-prosumer groups, relaxation of progressive electricity billing system, and share of renewable energy in the generation mix. The analysis reveals that households prefer installation of smart meter devices and introduction of E-prosumer groups, and are willing to pay 10,044 KRW/year (USD 8.97/year) and 5,222 KRW/year (USD 4.66/year), respectively, for these services. However, households dislike the participation of privately-owned companies under the competitive market structure, owing to the possibility of a rise in the electricity price.