This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven’t been made and which one the modified or variant PSO that will be developed.

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Particle Swarm Optimization: Technique, System and Challenges

https://vbn.aau.dk/ws/files/206009396/Correction.pdf

Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage

Phasor measurement units (PMUs) are rapidly being deployed in electric power networks across the globe. Wide-area measurement system (WAMS), which builds upon PMUs and fast communication links, is consequently emerging as an advanced monitoring and control infrastructure. Rapid adaptation of such devices and technologies has led the researchers to investigate multitude of challenges and pursue opportunities in synchrophasor measurement technology, PMU structural design, PMU placement, miscellaneous applications of PMU from local perspectives, and various WAMS functionalities from the system perspective. Relevant research articles appeared in the IEEE and IET publications from 1983 through 2014 are rigorously surveyed in this paper to represent a panorama of research progress lines. This bibliography will aid academic researchers and practicing engineers in adopting appropriate topics and will stimulate utilities toward development and implementation of software packages.

Synchrophasor Measurement Technology in Power Systems: Panorama and State-of-the-Art

Experimental validation of a real time energy management system for microgrids in islanded mode using a local day-ahead electricity market and MINLP

This paper proposes a multi-agent based differential evolution algorithm for solving economic dispatch problem considering ramp rate limit and prohibited operating zones. In this work, multiagent system (MAS) concept is integrated with differential evolution (DE) to form a multiagent based differential evolution (MADE) algorithm. In MADE, a member in the population is treated as an agent. They all live in a lattice like environment. Neighborhood competition and cooperation among agents and differential evolution approach make this algorithm reach optimum value quickly. The proposed MADE has been applied to three test systems namely 3unit, 6 unit and 15 unit systems and the results are compared with DE and existing approaches. Simulation results show that MADE gives better value and converges quickly to optimum values than earlier reported methods.

Multi-Agent Based Differential Evolution Algorithm for Economic Dispatch With Generator Constraints

Dynamic pricing and demand response programs are playing key roles in the modern smart grid environment to manage peak loads. With the current advent of communication systems, more user-friendly price driven schemes started emerging at the residential level. In the United States, heating and air-conditioning loads are major contributors to total electricity consumption in residential buildings. In some areas, cooling loads during summer months are the highest of the whole year due to high outdoor air temperature and the wholesale electricity prices also increase as a consequence. In this paper, two demand response strategies i.e, active control and transactive control for a residential building are analyzed. Heating, Ventilation and Air Conditioning (HVAC) is considered as a responsive load and its set-point change to real-time prices and total power consumption in each method are analyzed. Modelling of single-family residential building and simulation are done in GridLAB-D.

Analysis of Active and Transactive Demand Response Strategies for Smart Residential Buildings

The advent of smart meter technology has enabled periodic monitoring of consumer energy consumption. Hence, short term energy forecasting is gaining more importance than conventional load forecasting. An Accurate forecasting of energy consumption is indispensable for the proper functioning of a virtual power plant (VPP). This paper focuses on short term energy forecasting in a VPP. The factors that influence energy forecasting in a VPP are identified and an artificial neural network based energy forecasting model is built. The model is tested on Sydney/ New South Wales (NSW) electricity grid. It considers the historical weather data and holidays in Sydney/ NSW and forecasts the energy consumption pattern with sufficient accuracy.

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Short term energy forecasting techniques for virtual power plants

This paper describes the development of an IEC 61850 based substation automation system (SAS) architecture. IEC 61850 is a standard for the design of electrical SAS. It defines the communication between IEDs in the substation and the related system requirements. The GOOSE messaging, Substation Configuration description Language (SCL) makes IEC 61850 more reliable, and the bottlenecks faced like the one of interoperability have been addressed. In addition to lowering the installation and commissioning costs, a significant reduction in maintenance cost can also be achieved. It minimizes costs of technological obsolescence because of a global acceptance and adoption and future-proof concept of abstract services as well as independence of mapping to protocols. The above architecture is modeled using UML and XML. Lower equipment migration costs, reduced wiring, ease in integration and the ease in adding functionalities to the existing SAS makes IEC 61850 based SAS a widely accepted norm.

Modeling an IEC61850 based substation automation system

Microgrids incorporated with synchronous distributed generators (SDGs) contribute to the significant amount of fault currents compared to inverter-based DGs. During islanded mode also, the fault magnitudes are significant and call for efficient and fast protection schemes. The coordination protection devices, especially, directional overcurrent relays (DOCRs) is a vital issue when dealing with microgrids with SDGs. In addition to the connection of DGs, frequent line outages affect the DOCR settings and cause device maloperation leading to miscoordination issues. This paper deals with optimal coordination of DOCRs for microgrids considering DG and line outages. A coordination model is proposed which involves the constraints corresponding to all N-1 contingencies, which can be a result of line / DG outage. In addition, cases of multiple fault locations are also included in the model to provide effective settings under all circumstances. The problem is modeled as a linear interval programming (ILP) problem and solved using invasive weed optimization (IWO) method. The ILP problem is stated as a sub-problem to IWO which would make it a hybrid optimizer. The proposed method is tested on 9 bus microgrid test system and results are presented.

K. Shanti Swarup

Papers

Multi-Agent Based Differential Evolution Algorithm for Economic Dispatch With Generator Constraints

Analysis of Active and Transactive Demand Response Strategies for Smart Residential Buildings

Short term energy forecasting techniques for virtual power plants

Modeling an IEC61850 based substation automation system

Optimal Protection Coordination for Microgrid Considering DG and Line Outages Using IWO-ILP Technique