This paper presents a review of advanced control techniques for microgrids. This paper covers decentralized, distributed, and hierarchical control of grid-connected and islanded microgrids. At first, decentralized control techniques for microgrids are reviewed. Then, the recent developments in the stability analysis of decentralized controlled microgrids are discussed. Finally, hierarchical control for microgrids that mimic the behavior of the mains grid is reviewed.

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Advanced Control Architectures for Intelligent Microgrids—Part I: Decentralized and Hierarchical Control

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Optimization methods applied to renewable and sustainable energy: A review

Large-scale deployment of intermittent renewable energy (namely wind energy and solar PV) may entail new challenges in power systems and more volatility in power prices in liberalized electricity markets. Energy storage can diminish this imbalance, relieving the grid congestion, and promoting distributed generation. The economic implications of grid-scale electrical energy storage technologies are however obscure for the experts, power grid operators, regulators, and power producers. A meticulous techno-economic or cost-benefit analysis of electricity storage systems requires consistent, updated cost data and a holistic cost analysis framework. To this end, this study critically examines the existing literature in the analysis of life cycle costs of utility-scale electricity storage systems, providing an updated database for the cost elements (capital costs, operational and maintenance costs, and replacement costs). Moreover, life cycle costs and levelized cost of electricity delivered by electrical energy storage is analyzed, employing Monte Carlo method to consider uncertainties. The examined energy storage technologies include pumped hydropower storage, compressed air energy storage (CAES), flywheel, electrochemical batteries (e.g. lead–acid, NaS, Li-ion, and Ni–Cd), flow batteries (e.g. vanadium-redox), superconducting magnetic energy storage, supercapacitors, and hydrogen energy storage (power to gas technologies). The results illustrate the economy of different storage systems for three main applications: bulk energy storage, T&D support services, and frequency regulation.

Electrical energy storage systems: A comparative life cycle cost analysis

Due to the stochastic nature of wind, electric power generated by wind turbines is highly erratic and may affect both the power quality and the planning of power systems. Energy Storage Systems (ESSs) may play an important role in wind power applications by controlling wind power plant output and providing ancillary services to the power system and therefore, enabling an increased penetration of wind power in the system. This article deals with the review of several energy storage technologies for wind power applications. The main objectives of the article are the introduction of the operating principles, as well as the presentation of the main characteristics of energy storage technologies suitable for stationary applications, and the definition and discussion of potential ESS applications in wind power, according to an extensive literature review.

A review of energy storage technologies for wind power applications

The paper reviews different approaches, technologies, and strategies to manage large-scale schemes of variable renewable electricity such as solar and wind power. We consider both supply and demand side measures. In addition to presenting energy system flexibility measures, their importance to renewable electricity is discussed. The flexibility measures available range from traditional ones such as grid extension or pumped hydro storage to more advanced strategies such as demand side management and demand side linked approaches, e.g. the use of electric vehicles for storing excess electricity, but also providing grid support services. Advanced batteries may offer new solutions in the future, though the high costs associated with batteries may restrict their use to smaller scale applications. Different “P2Y”-type of strategies, where P stands for surplus renewable power and Y for the energy form or energy service to which this excess in converted to, e.g. thermal energy, hydrogen, gas or mobility are receiving much attention as potential flexibility solutions, making use of the energy system as a whole. To “functionalize” or to assess the value of the various energy system flexibility measures, these need often be put into an electricity/energy market or utility service context. Summarizing, the outlook for managing large amounts of RE power in terms of options available seems to be promising.

https://research.aalto.fi/files/6557604/lund_et_al_review.pdf

Review of energy system flexibility measures to enable high levels of variable renewable electricity

Wind energy is not constant and windmill output is proportional to the cube of wind speed, which causes the generated power of wind turbine generators (WTGs) to fluctuate. In order to reduce fluctuation, different methods are available to control the pitch angle of blades of windmill. In a previous work, we proposed the pitch angle control using minimum variance control, and output power leveling was achieved. However, it is a controlled output power for only rated wind speed region. This paper presents a control strategy based on average wind speed and standard deviation of wind speed and pitch angle control using a generalized predictive control in all operating regions for a WTG. The simulation results by using actual detailed model for wind power system show the effectiveness of the proposed method.

Output power leveling of wind turbine Generator for all operating regions by pitch angle control

In recent years, distributed generation, as clean natural energy generation and cogeneration system of high thermal efficiency, has increased due to the problems of global warming and exhaustion of fossil fuels. Many of the distributed generations are set up in the vicinity of the customer, with the advantage that this decreases transmission losses. However, output power generated from natural energy, such as wind power, photovoltaics, etc., which is distributed generation, is influenced by meteorological conditions. Therefore, when the distributed generation increases by conventional control techniques, it is expected that the voltage change of each node becomes a problem. Proposed in this paper is the optimal control of distribution voltage with coordination of distributed installations, such as the load ratio control transformer, step voltage regulator (SVR), shunt capacitor, shunt reactor, and static var compensator. In this research, SVR is assumed to be a model with tap changing where the signal is received from a central control unit. Moreover, the communication infrastructure in the supply of a distribution system is assumed to be widespread. The genetic algorithm is used to determine the operation of this control. In order to confirm the validity of the proposed method, simulations are carried out for a distribution network model with distributed generation (photovoltaic generation).

Optimal Distribution Voltage Control and Coordination With Distributed Generation

This paper presents an output power smoothing method by a simple coordinated control of DC-link voltage and pitch angle of a wind energy conversion system (WECS) with a permanent magnet synchronous generator (PMSG). The WECS adopts an AC-DC-AC converter system with voltage-source converters (VSC). The DC-link voltage command is determined according to output power fluctuations of the PMSG. The output power fluctuationsin low- and high-frequency domains are smoothed by the pitch angle control of the WECS, and the DC-link voltage control, respectively. By using the proposed method, the wind turbine blade stress is mitigated as the pitch action in high-frequency domain is reduced. In addition, the DC-link capacitor size is reduced without the charge/discharge action in low-frequency domain. A chopper circuit is used in the DC-link circuit for stable operation of the WECS under-line fault. Effectiveness of the proposed method is verified by the numerical simulations.

A Coordinated Control Method to Smooth Wind Power Fluctuations of a PMSG-Based WECS

Utilization of renewable energy are coming up from view points of environmental conservation and depletion of fossil fuel. However, the generated power from renewable energies is always fluctuating due to environmental status. Energy storage system is indispensable to compensate these fluctuating components. Energy capacitor system (ECaSS) connected an electric double-layer capacitor (EDLC) with power-electronics devices is useful for the compensation of fluctuating power since one is capable of controlling both active and reactive power simultaneously. This paper proposes the current-source ECaSS (CS-ECS), which consists of EDLC, bi-directional DC-DC converter, and current-source inverter. We have presented the control system for the active/reactive power control of CS-ECS, and have shown the effectiveness of CS-ECS through computer simulations for case of wind power generation system.

Output levelling of renewable energy by electric double-layer capacitor applied for energy storage system

Price forecasting in competitive electricity markets is critical for consumers and producers in planning their operations and managing their price risk, and it also plays a key role in the economic optimization of the electric energy industry. This paper explores a technique of artificial neural network (ANN) model based on similar days (SD) method in order to forecast day-ahead electricity price in the PJM market. To demonstrate the superiority of the proposed model, publicly available data acquired from the PJM Interconnection were used for training and testing the ANN. The factors impacting the electricity price forecasting, including time factors, load factors, and historical price factors, are discussed. Comparison of forecasting performance of the proposed ANN model with that of forecasts obtained from similar days method is presented. Daily and weekly mean absolute percentage error (MAPE) of reasonably small value and forecast mean square error (FMSE) of less than 7$/MWh were obtained for the PJM data, which has correlation coefficient of determination of 0.6744 between load and electricity price. Simulation results show that the proposed ANN model based on similar days method is capable of forecasting locational marginal price (LMP) in the PJM market efficiently and accurately.

Naomitsu Urasaki

Papers

Output power leveling of wind turbine Generator for all operating regions by pitch angle control

Optimal Distribution Voltage Control and Coordination With Distributed Generation

A Coordinated Control Method to Smooth Wind Power Fluctuations of a PMSG-Based WECS

Output levelling of renewable energy by electric double-layer capacitor applied for energy storage system

A Novel Approach to Forecast Electricity Price for PJM Using Neural Network and Similar Days Method