Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

The development of distributed renewable energy,such as photovoltaic power and wind power generation,makes the energy system cleaner,and is of great significance in reducing carbon emissions.However,weather can affect distributed renewable energy power generation,and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy.Energy systems with high penetration of distributed renewable energy involve the high-dimensional,nonlinear dynamics of large-scale complex systems,and the optimal solution of the uncertainty model is a difficult problem.From the perspective of statistical machine learning,the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System:Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

The variability of the output power of distributed renewable energy sources(DRESs)that originate from the fastchanging climatic conditions can negatively affect the grid stability.Therefore,grid operators have incorporated ramp-rate limitations(RRLs)for the injected DRES power in the grid codes.As the DRES penetration levels increase,the mitigation of high-power ramps is no longer considered as a system support function but rather an ancillary service(AS).Energy storage systems(ESSs)coordinated by RR control algorithms are often applied to mitigate these power fluctuations.However,no unified definition of active power ramps,which is essential to treat the RRL as AS,currently exists.This paper assesses the various definitions for ramp-rate RR and proposes RRL method control for a central battery ESS(BESS)in distribution systems(DSs).The ultimate objective is to restrain high-power ramps at the distribution transformer level so that RRL can be traded as AS to the upstream transmission system(TS).The proposed control is based on the direct control of theΔP/Δt,which means that the control parameters are directly correlated with the RR requirements included in the grid codes.In addition,a novel method for restoring the state of charge(So C)within a specific range following a high ramp-up/down event is proposed.Finally,a parametric method for estimating the sizing of central BESSs(BESS sizing for short)is developed.The BESS sizing is determined by considering the RR requirements,the DRES units,and the load mix of the examined DS.The BESS sizing is directly related to the constant RR achieved using the proposed control.Finally,the proposed methodologies are validated through simulations in MATLAB/Simulink and laboratory tests in a commercially available BESS.

Proposed framework for blockchain technology in a decentralised energy network

The integration of distributed renewable energy sources into the conventional power grid has become a hot research topic, all part of attempts to reduce greenhouse gas emission. There are many distributed renewable energy sources available and the network participants in energy delivery have also increased. This makes the management of the new power grid with integrated distributed renewable energy sources extremely complex. Applying the technical advantages of blockchain technology to this complex system to manage peer-to-peer energy sharing, transmission, data storage and build smart contracts between network participants can develop an optimal consensus mechanism within the new power grid. This paper proposes a new framework for the application of blockchain in a decentralised energy network. The microgrid is assumed to be private and managed by local prosumers. An overview description of the proposed model and a case study are presented in the paper.

Potential and roadmap of CO_(2) emission reduction in urban buildings: Case study of Shenzhen

Buildings play an increasingly important role to determine the trend of CO_(2) emissions in cities.Whether CO_(2) emissions from buildings can be effectively mitigated has great significance for cities to achieve climate governance goals.The study takes Shenzhen,a China's megacity,as an example to examine how the penetration of newly emerging clean technologies and consumer-to-prosumer role transition of buildings will contribute to CO_(2) emission reductions.Based on a Low Emissions Analysis Platform(LEAP)model,the major results indicate that CO_(2) emissions of Shenzhen's building sector could be capped by 2022-2025 and substantially decreased by more than 60%by 2030.Acelerating energy efficiency retroftting of existing buildings and enforcing stricter design standards on new buildings could largely reduce CO_(2) emissions,but still unable to prevent them from growing.The intensification of building energy-saving management and promotion of distributed renewable energy use would bring additional potentials of emission reduction,enabling a peak-reaching and a rapid downward trend of building emissions.To achieve the potentials,close cooperation and synergic efforts between multiple stakeholders are advocated for establishing inteligent energysaving management systems,decarbonizing urban power supply,and popularizing distributed roftop photovoltaic power stations.