Utilization Of Renewable Energy Generation Research Articles

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Optimal configuration of energy storage for alleviating transmission congestion in renewable energy enrichment region

High-penetration renewable energy development causes transmission congestion in power system operation. Such transmission congestion in short period can be alleviated by energy storage configuration, instead of investing and expanding new transmission lines. This paper presents an optimal configuration method of energy storage for alleviating transmission congestion in renewable energy enrichment region. In order to obtain the characteristics of renewable energy outputs, a scenario generation technique for renewable energy outputs considering spatio-temporal correlation is first proposed. A Monte Carlo-based approach for evaluating the transmission congestion is proposed for identifying the potential locations of energy storage installation. Finally, an optimal configuration method of energy storage based on stochastic programming is proposed for alleviating transmission congestion. Numerical experiments are carried out on a modified IEEE-RTS 24-bus system and a practical 129-bus system. Numerical results show that energy storage can improve the flexibility of power system operation and the utilization of renewable energy generation. Especially, in the practical 129-bus system, the proposed method reduces renewable energy curtailment by 625.26 MWh and 444.43 MWh under fall and winter typical scenarios, respectively. And, investing some energy storage devices is more economical when the duration of transmission congestion is relatively short.

Design and HIL Realization of an Online Adaptive Dynamic Programming Approach for Real-Time Economic Operations of Household Energy Systems

Stochastic, nonlinear and time-varying factors bring great challenges to accurate modeling and design of real-time (RT) energy management systems (EMSs) for modern energy systems with intermittent renewable energy sources. In this paper, we propose a novel model-free RT-EMS based on the predictive adaptive dynamic programming (ADPredictive) algorithm for energy scheduling problems in a home EMS environment. The proposed RT-ADPreditive EMS can minimize the total cost of electricity, reduce battery life loss, and maximize the utilization of renewable energy generation. In the proposed RT-ADPredictive EMS, the gated recurrent unit (GRU) neural network (NN) is employed to perform online prediction of renewable energy generation and load consumption of the household based on RT data. The RT-ADPreditive algorithm can approximate the performance index function and the optimal control law based on the RT data and predicted data for the next time step. Convergence of the proposed method is mathematically proven, and a hardware-in-the-loop (HIL) experimental platform comprising dSPACE and RT-lab is built to verify the effectiveness of the proposed RT-ADPreditive home EMS.

Hotspot Detection Method in Large Capacity Photovoltaic (PV) Farm

The obligation to use low carbon emissions power plants encourages the increased utilization of renewable energy generation. Among the whole renewable energy plants, photovoltaic (PV) is a modular plant that is easy to implement, which the utilization reaches 100GW in the year 2017. By the increasing use of PV globally, the health of PV modules needs to be a concern because, during the operation, PV modules can experience various faults. Almost 50% of the overall fault is the hotspot which is very hard to detect on a wide area PV farm. For example, a 30 MW PV generation with an area of 60 hectares and composed of 126000 modules (consists of millions of cell), the existing hotspot detection methods takes up to 210 days. The long time and not continuous detection lets the hotspot to degrade and burn the modules. To prevent catastrophic failure due to hotspot, a detection method that can detect the fault quickly is needed. The proposed method, thermal imaging using a fish-eye lens could be used in this case as it has a very wide angle of view, which allows monitoring all of the PV modules in one detection period.

A distributed real-time control algorithm for energy storage sharing

In this paper, energy storage sharing among a group of cooperative households with integrated renewable generations in a grid-connected microgrid in the presence of dynamic electricity pricing is studied. In such a microgrid, a group of households, who are willing to cooperatively operate a shared energy storage system (ESS) via a central coordinator, aims to minimize their long term time-averaged costs, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar energy generations and time-varying load requests from all households, as well as the fluctuating electricity prices. We formulate this energy management problem, which comprises storage management and load control, as a constrained stochastic programming problem. Based on the Lyapunov theory, a distributed real-time sharing control algorithm is proposed to provide a suboptimal solution for the constrained stochastic programming problem without requiring any system statistics. The proposed distributed real-time sharing control algorithm, in which each household independently solves a simple convex optimization problem in each time slot, can quickly adapt to the system dynamics. The performance of the proposed low-complexity sharing control algorithm is evaluated via both theoretical analysis and numerical simulations. By comparing with a greedy sharing algorithm and the distributed ESSs case, it is shown that the proposed distributed sharing control algorithm outperforms in terms of both cost saving and renewable energy generation utilization.

How Does Information and Communication Technology Capital Affect Productivity in the Energy Sector? New Evidence from 14 Countries, Considering the Transition to Renewable Energy Systems

By focusing on a distributed energy system that has been widely diffused for efficient utilization of renewable energy generation in recent years, this paper investigates the relationship between productivity growth and information and communications technology capital in the energy sector. Information and communications technology is a key factor in operating distributed energy systems in a way that balances energy supply and demand in order to minimize energy loss and to enhance capacity utilization. The objective of this study is to clarify the determining factors that affect productivity growth, focusing on three different information and communications technologies: information technology capital, communication technology capital and software capital. Our estimation sample covers energy sectors in 14 countries from 2000 to 2014. The results show that information technology and software capital contribute to increasing material productivity and capital productivity in the energy sector, respectively. Meanwhile, communication technology capital negatively affects these two productivity indicators.

Credit-Based Distributed Real-Time Energy Storage Sharing Management

In this paper, energy storage sharing among a group of cooperative households with integrated renewable generations in a grid-connected microgrid is studied. In such a microgrid, a group of households, who are willing to cooperatively operate a shared energy storage via a central coordinator, aims to minimize their long term time-averaged costs, by jointly taking into account the operational constraints of the shared energy storage, the stochastic solar power generations and the time-varying load demands from all households, as well as the fluctuating electricity prices. This energy management problem, which comprises storage management and load control, is first formulated as a constrained stochastic programming problem. Based on the Lyapunov optimization theory, a distributed real-time sharing control algorithm is proposed to solve the constrained stochastic programming problem without requiring any statistical knowledge of the stochastic renewable energy generations and the uncertain power loads. The credit-based distributed sharing algorithm, in which each household independently solves a simple convex optimization problem without requiring any statistics of the system, is designed to quickly adapt to the system dynamics while facilitating a fair allocation of the shared energy storage with respect to individual households’ energy contributions. The performance of the proposed low-complexity distributed sharing algorithm is evaluated via theoretical analysis. Numerical simulations using a practical system setup are conducted to investigate the effectiveness of the proposed sharing control algorithm in terms of energy cost saving and fairness. The simulation results show that the proposed credit-based distributed sharing algorithm can save power consumption cost by coordinating the use of the shared battery among households in a fair manner while improving the utilization of renewable energy generation.

Optimal dispatch of hybrid energy islanded microgrid considering V2G under TOU tariffs

In order to achieve a prospective economic effect of renewable energy generations and vehicle to grid (V2G), this paper proposes an optimal dispatch method of wind-PV-battery microgrid considering V2G under time-of-use (TOU) tariffs for those isolated communities in remote islands and mountainous areas. A cooperative dispatch strategy and an optimal dispatch model are both presented for the total operation cost minimization and higher utilization of renewable energy generation. Finally, the simulation results show that the proposed method is effective and feasible.

A Distributed Control Method With Minimum Generation Cost for DC Microgrids

This paper proposes a fully distributed control method for dc microgrids to realize power balance and bus voltage recovery without central coordination. Based on peer-to-peer communication, distributed generators and energy storage systems need only exchange information between neighbors over a sparse communication network, and hence there is a minimal communication burden. By combining the equal increment rate criteria and a subgradient algorithm, this distributed control method can efficiently regulate the bus voltage to the nominal value with minimum generation cost. In addition, the proposed method can improve the utilization of renewable energy generation via reasonable power sharing among distributed generators. Both islanded mode and grid-connected mode were simulated as case studies. The results indicate that the method is reliable, scalable, and flexible over centralized schemes. Influences of communication delays and failures of communication links are also discussed in numerical tests.

Distributed optimal dispatch of virtual power plant based on ELM transformation

To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dispatch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.