1,246 research outputs found
Enhancing DC microgrid performance through machine learning-optimized droop control
A machine learning-based optimized droop method is suggested here to simultaneously reduce the production cost (PC) and power line losses (PLL) for a class of direct current (DC) microgrids (MGs). Traditionally, a communication-less technique known as the hybrid droop method has been employed to decrease PC and PLL in DC MGs. However, achieving the desired reduction in either PC or PLL requires arbitrary adjustments of weighting coefficients for each distributed generator in the conventional hybrid droop method. To address this challenge, this paper introduces a systematic approach that capitalizes on the benefits of artificial intelligence to accurately predict both the PC and PLL in a DC MG. Furthermore, an optimization technique relying on the gradient descendent method is employed to independently optimize both PC and PLL for each scenario. The effectiveness of the proposed method is confirmed through a comparative study with classical and hybrid droop coordination schemes under various scenarios such as rapid load changes.© 2024 The Authors. IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivsLicense, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.fi=vertaisarvioitu|en=peerReviewed
Adaptive Electricity Scheduling in Microgrids
Microgrid (MG) is a promising component for future smart grid (SG)
deployment. The balance of supply and demand of electric energy is one of the
most important requirements of MG management. In this paper, we present a novel
framework for smart energy management based on the concept of
quality-of-service in electricity (QoSE). Specifically, the resident
electricity demand is classified into basic usage and quality usage. The basic
usage is always guaranteed by the MG, while the quality usage is controlled
based on the MG state. The microgrid control center (MGCC) aims to minimize the
MG operation cost and maintain the outage probability of quality usage, i.e.,
QoSE, below a target value, by scheduling electricity among renewable energy
resources, energy storage systems, and macrogrid. The problem is formulated as
a constrained stochastic programming problem. The Lyapunov optimization
technique is then applied to derive an adaptive electricity scheduling
algorithm by introducing the QoSE virtual queues and energy storage virtual
queues. The proposed algorithm is an online algorithm since it does not require
any statistics and future knowledge of the electricity supply, demand and price
processes. We derive several "hard" performance bounds for the proposed
algorithm, and evaluate its performance with trace-driven simulations. The
simulation results demonstrate the efficacy of the proposed electricity
scheduling algorithm.Comment: 12 pages, extended technical repor
Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid
This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units. We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variability indicators, based on Sharpe Ratio, are introduced for analyzing the volatility of the energy storage schedules. By adding these additional criteria, energy storage planning is optimized seeking to minimize the following: total Microgrid (MG) costs; PEVs batteries usage; maximum peak load; difference between extreme scenarios and two Sharpe Ratio indices. Different scenarios are considered, which are generated with the use of probabilistic forecasting, since prediction involves inherent uncertainty. Energy storage planning scenarios are scheduled according to information provided by lower and upper bounds extracted from probabilistic forecasts. A MicroGrid (MG) scenario composed of two renewable energy resources, a wind energy turbine and photovoltaic cells, a residential MG user and different PEVs is analyzed. Candidate non-dominated solutions are searched from the pool of feasible solutions obtained during different Branch and Bound optimizations. Pareto fronts are discussed and analyzed for different energy storage scenarios. Perhaps the most important conclusion from this study is that schedules that minimize the total system cost may increase maximum peak load and its volatility over different possible scenarios, therefore may be less robust
A Novel use of the Hybrid Energy Storage System for Primary Frequency Control in a Microgrid
High penetration of renewable energycauses fluctuationsof power flow and results in system frequency fluctuation, which significantlyaffects the power system operation. The situation in microgrid (MG) is worse because of the low inertia and small time constant of the system. This paper present a novel use ofthesuperconducting magnetic energy storage (SMES) and battery hybrid energy storage system with the function of frequency control in the MG.A hybrid power management strategy for the SMES and the battery is used to achieve, firstly, a faster primary frequencycontroland secondly, an improvement of battery service time
Dynamic Pricing for Microgrids Energy Transaction in Blockchain-based Ecosystem
Microgrid (MG) is an efficient platform to integrate distributed energy resources in distribution networks. The operation of MG is also expected to take advantage of emerging smart grid technologies to improve operation and robustness. Among these emerging technologies, blockchain technology provide a big potential to rule the energy transaction in an innovative way. In this paper, a physical architecture of the ecosystem with MGs is firstly presented. Moreover, as the main parts of the blockchain technology, the operation of distributed ledger and smart contracts are introduced in the transaction process. Considering dynamic pricing scheme in the process of energy transaction in the ecosystem, we model the energy transaction between MGs and distribution system operator (DSO) to decide the trading amount and price of the energy. The welfare maximization mathematical model is established accordingly, and the formulated dual problem will be used to find the shadow price of selling renewable energy to grid and real-time retailer price from DSO. Finally, with the deployment of distribution ledger, the energy transaction process can be fully recorded, and transaction execution can be achieved with the help of smart contracts. In light of the mentioned perspective, beside demonstrated benefit brought to both MGs and DSO, the energy transaction and management based on the blockchain will result in higher reliability and improved auditability in the ecosystem
Review on Power Quality Enhancement and its effects on Micro Grid
Power generation through the renewable energy sources has become more viable and economical than the fossil fuel based power plants. By integrating small scale distributed energy resources, microgrids are being introduced as an alternative approach in generating electrical power at distribution voltage level. The power electronic interface provides the necessary flexibility, security and reliability of operation between micro-sources and the distribution system. The presence of non-linear and the unbalanced loads in the distribution system causes power quality issues in the Microgrid system. This paper explores and reviews different control strategies developed in the literature for the power quality enhancement in microgrids
- …