The general concept of AC Optimal Power Flow (ACOPF) refers to the economic dispatch planning under electric network constraints. Moreover, each instance with the entire network must be solved in real-time (i.e., every five minutes) to ensure cost-effective power system operation while satisfying power balance equation. As the operation of power systems penetrated with intermittent renewable energy becomes more complicated, this paper proposes Deep Neural Network (DNN) and Levenberg-Marquardt backpropagation-based Twin Delayed Deep Deterministic Policy Gradient (TD3) approach to improve computational performance of ACOPF. Specifically, because the ACOPF model shall consider prevailing constraints of the power system, including power balance equation, we set the appropriate reward vector in the training process to build our own policy. Furthermore, we add random Gaussian noise to individual net loads for representing uncertainty characteristics introduced by renewable energy sources. Finally, the proposed model is compared with the MAT-POWER solution on the IEEE 118-bus system to demonstrate its efficacy and robustness.

We propose an optimal operation strategy for an electric vehicle (EV) aggregator (AGG), which performs energy arbitrage in the energy market and provides ancillary services from aggregated EVs, while providing charging services to EVs to maximize the profit in a future energy market. We group EV batteries as several virtual batteries (VB) with respect to their departure time and stages to implement multiple schedules simultaneously in the multi-stage stochastic optimization (MSSO). They are continuously grouped and regrouped as EVs arrive and depart. We predict VB scenario trees of stepwise EV driving routes and optimize the decisions in responding to uncertain EV movements. The VB states change as EVs enter and exit through the stages, so we should indirectly track time-varying VB characteristics at each stage. Then, we distribute the regulation bids for the VBs to the individual EVs. To reduce mismatches between the bidding amounts for VBs and actual transacted amounts for EVs, we suggest a novel binary progressive hedging algorithm to quickly determine the VB operations. Based on data from historical vehicle statistics, we verify that our strategy with the MSSO model provides a higher profit to the AGG than two-stage stochastic models.

The depletion of existing resources and environmental problems led to an increase in distributed energy resources (DERs) and the accordingly emergence of the prosumer. This change has created an issue of power system operations and the coordinating entities of distribution networks. Besides, environmental concerns and the global policy of supporting renewable energy resources has given some electricity consumers a better perception and a willingness to pay (WTP) for green energy at a higher cost. This article presents a bidding-based peer-to-peer (P2P) energy transaction optimization model that considers these green energy preferences based on prosumer and consumer's intention. The objective of the model, which deals with energy transactions between prosumers and consumers within the virtual energy community, is to minimize community operating costs taking into account the depreciation costs of energy storage units while maximizing the social welfare for encouraging the energy trading among consumers and prosumers. The weighted social-welfare terms in the objective function provides diverse community operations reflecting the goal of the energy trading operator (community operator). Case studies were conducted according to the presence and absence of energy transactions and energy storages and the green energy preference. Results of the case study showed that the benefits of prosumers and community have occurred in energy transactions and CO2 emission is reduced according to green energy preference.

South Korea announced an energy transition roadmap, CO₂ roadmap, and national greenhouse gas reduction target of nationally determined contribution (NDC) for the Paris Agreement. Furthermore, the government has also set a goal of reducing its CO₂ emissions to reach net-zero carbon emissions by 2050. Additionally, the Korean government submitted an enhanced update of the first NDC at the end of 2021. In the electricity sector, the updated NDC proposed the GHG emissions target of 149.9 million tons in 2030. In this study, we model eight scenarios based on future energy mix and demand forecast considering the government’s latest plans to evaluate the possible emission reduction and impacts in the electricity sector. The scenario-based analysis is conducted to check whether it can satisfy the CO₂ reduction target by using PLEXOS, a production simulation model. The results show that emission reduction targets are difficult to accomplish in the short term and can lead to significant changes in the operation of generators and increased costs to realize the decarbonization pathway.

The high penetration level of renewable energy in large-scale power systems could adversely affect power quality, such as voltage stability and harmonic pollution. This paper assesses the impacts of Distribution Static Compensator (D-STATCOM), one of the Flexible AC Transmission System (FACTS) devices, on power quality of 4.16kV-level distribution systems via transient and steady-state analysis. Carrier-based Pulse Width Modulation (PWM) control in D-STATCOM generates d-q axis current reference via the PID (Proportional-Integral-Differential) controller to control d-q axis current and voltage. A new control method, via the Deep Deterministic Policy Gradient (DDPG) algorithm-based reinforcement learning (RL), is studied to create a new d-q axis current reference applying to the voltage control, which can improve voltage stability and transient response and derive fast convergence of current and voltage at the D-STATCOM bus. The real-time simulations on an IEEE 13-bus system show that the proposed approach can better control the D-STATCOM than the conventional control methods for enhancing voltage stability and transient performance.

This article investigates customer-side energy storage system operations to minimize the electricity bill under a peak load limitation constraint and uncertain environments. Specifically, it is discussed how the demand and price uncertainties impact the system performance. It is shown that the energy storage system operation based on the Markov decision process with stochastic information has near-optimum performance, which is achieved by an iterative method with perfect information when the electricity price and demand are slightly varied. To address a problem, such as the failure of peak load reduction due to high uncertainties, two heuristic methodologies are suggested by modifying the peak load threshold and the charge/discharge reservation quantity. It is demonstrated that the proposed approach can effectively manage the uncertainties with marginal performance degradation.