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Integration of Renewable Energy Sources in Electrical System and Management Issues
General Introduction
Faced to energy challenges, primary energy demand in the world wide is growing. According to the IEA (International Energy Agency), the world’s energy needs could be 50% higher in 2030 than those of 2011 [1]. However, the stock of oil and coal in our planet will be exhausted in next few decades with the same rate of current utilization.
Nowadays, global warming becomes more serious due to greenhouse effects. Some emissions of green-house gases, of course, come from human activities, for example the car emissions. Nevertheless, the production and processing of electrical energy is one of the main sources of greenhouse gases, especially for developing countries like China, India and Latin America [1]. To reduce greenhouse gas emissions and assure energy security, renewable energies are greatly required in the electrical power production.
Today different kinds of renewable energy technologies are established in world markets. Some renewable energy technologies, such as wind turbines, are becoming quickly competitive in growing markets, and some are widely recognized as the lowest cost option for stand-alone and off-grid applications, especially in islands where fuel and cost are expensive. The capital costs of certain renewable energy technologies have been obviously reduced over the last decade and may continually decreased over the next few decades. According to the “World Energy Outlook in 2013”, there will be a massive growth of the renewable energy in the next 25 years because of fossil fuel crisis, energy production security, economic growth, and danger of environment deterioration [1].
However, an electrical generating system depending entirely on the fluctuating renewable energy sources (RES), such as wind and solar photovoltaic (PV) power, is not reliable because of their poor availability and intermittent nature. Compared to the conventional oil and coal based power plants, they cannot provide ancillary services (AS) to participate to the management of electrical power systems. Decentralized energy production is increasing on the basis of cogeneration units, RES or conventional generations, which have been installed by independent producers. In last two decades, research interests have been focused on the microgrids (smart grids) and the integration of distributed generations to increase the RES penetration level. Therefore, by considering the electrical system uncertainties (from both generation and consumption), this research work focuses on the integration of renewable energies in electrical systems.
In this chapter, some background knowledge and basic information about RES are previously presented. Then, problems, which result from the integration of this type of decentralized production in power systems, are explained and perspectives to solve them are listed. As the development of this considered “small size” of production is devoted to be consumed by local loads, concepts of smart cities, smart grids and microgrids are briefly introduced. As the power intermittency and the energy availability are weak points of conventional passive ES based generators, new hardware technologies are then considered to balance these drawbacks: storage systems, hybrid active generators and micro gas turbines (MGT). Thus, the energy management of future electrical systems must be adapted or changed in order to use these technologies. Hence, the energy management of a microgrid is presented to well understand the control architecture and functions. Finally, the research objectives and explored methods are summarized.
Renewable Energy Sources (RES)
Introduction
RES are defined as energy resources naturally regenerated over a short time scale. They are derived directly from the sun (such as solar thermal and photovoltaic), indirectly from the sun (such as wind, hydropower and photosynthetic energy stored in biomass), or from other natural movements and mechanisms of the environment (such as hydropower, geothermal and ocean energy). Conventional energy sources, which are based on oil, coal, and natural gas have proven to be highly effective, but at the same time are not environment-friendly and also are available in a limited quantity in one plant. While the potential of RES, such as wind, solar, biomass and geothermal, are enormous as they can meet many times the energy demand in the world [18].
RES development constitutes an energy political strategy to solve many problems like: climate change, global energy demand increase, limits of fossil fuel reserves, energy dependency, and low efficiency of the electric system. Moreover, the renewable technologies are becoming increasingly cost competitive in a number of countries and circumstances. RES power generation capacity is estimated to have increased by 128 GW in 2014, 37% is coming from the wind power, almost one-third solar power and more than a quarter from hydropower [19].
The Issue of RES: Benefits
As described in Appendix I, significant progresses in cost reduction have been made by wind and PV systems. While biomass, geothermal, and solar thermal technologies are also experiencing cost reductions and these are forecast to continue. These RES contribute to the energy supply portfolio diversity and provide alternative options to customers. In addition, RES are environmentally benign and contribute to a healthy economy by employment and investment opportunities. Although it is easy to recognize the advantages of utilizing alternative renewable energy forms, the different renewable sources are limited by different constraints depending on their intrinsic characteristics [19-23]. This part presents some benefits.
A. A vast and inexhaustible energy supplying
As the used energy is renewable, it is therefore sustainable and so will never run out. Strong winds, shining sun, and heat underneath earth surface can provide a constant energy supply.
B. Environmental benign with little to no global warming emissions
During the RES operating, they are clean and induce in little to no greenhouse and waste products such as carbon dioxide or other chemical pollutants, so have minimal impacts on the environment and human health.
According to [24], there are several mechanisms for potential reductions in electricity and CO2 emissions: shifting load to get a more efficient electrical system, support from electric vehicles and plug-in hybrid electric vehicles, advanced voltage control, support penetration of renewable wind and solar generation, and etc. Among those mechanisms, the support penetration of renewable energies contributes to the most significant part: with a higher renewable portfolio standard, less CO2 emission will be produced.
As it can be seen on the Figure I-1, since 1990, the CO2 emission is highly related to the rising electricity demand. However, this link will be broken from now and the world CO2 emissions from power generation will remain broadly flat through to 2030, even though the planet electricity demand is continually increasing. During this time, power generation related CO2 emissions within developed countries like European Union and United State will decrease, while that in the developing countries will increase, especially in China and India [25]. The reason for this link broken is the massive development of the green energy sources.
C. Economic benefits
First, increasing renewable energy has the potential to create new jobs. Then, it offers other trade of technologies and services development benefits. What’s more, RES require a less amount of maintenance, which reduces the costs. Finally, as most facilities are located away from urban centers of the capital cities, it will be profitable for regional areas, local services, and also tourism.
D. Energy security
The costs of renewable energy technologies have declined steadily and will be similar with those of fossil fuel generation in a near future [25]. Once built, the most of the RES operates at very low cost. What’s more, faced with challenges of energy crisis, RES can relieve some of the increasing of energy demand and reduce dependence on foreign sources. Moreover, distributed systems, as wind and solar, are less prone to large-scale failure because of their modular structure.
Constraints and limitations
Hydropower and geothermal power are naturally limited because of their geographic sites. Biomass requires large places for natural resources storage. For these resources, a large amount of units is needed to meet up with the large quantities of electricity produced by fossil fuels.
Another important limitation of RES is that they rely heavily upon the weather conditions: wind and sunshine. Unfortunately these RES are intermittent power sources (Figure I-2(a)). The electricity production from solar sources depends on the amount of solar irradiance. Solar output varies throughout the day and through the seasons because of the earth’s rotation plus its motion around the sun, and is affected by cloud cover. Wind power depends on wind speeds, air density and turbine characteristics (among other factors). These powers are not always available when necessary, such as solar in the night and wind power when the wind is not blowing.
The addition of intermittent resources causes large amounts of variable power. It decreases power system reliability. For example, when the PV power is injected into the electric system, the voltage at that location might increase beyond the acceptable range. Moreover, PV power inverters currently inject only real power into the electrical system. Therefore, advanced PV inverters are needed to absorb or inject reactive power to reduce the voltage impacts and may avoid the need for additional voltage-regulation equipment.
Moreover, the nature of the intermittency is, of course, different for the respective renewable energy technologies. Therefore, this difference could be a relevant factor to mitigating impacts. As the percentages of intermittent generation capacity become more and more significant, additional uncertainty is created in the management of the electrical system balance (between demand and generation) in real time. Increased amounts of conventional power reserve capacity are required and must be available immediately (spinning reserve) from plants capable of providing AS. These AS are required to manage the electrical power system securely.
Actual wind and PV generators must be considered as passive generators and they cannot participate to the grid management because of their variability and uncertainty [33]. As the percentage of intermittent RES generation capacity in a grid increases and becomes more significant, additional uncertainty is created in the management of real-time generation and consumption balance. This may require the increasing amount of conventional power reserve (spinning reserve) to manage the grid securely [26].
RES output power directly depends on the availability of their intermittent primary energy and so cannot be set to a prescribed value given by a grid operator. In most of the time, RES generators work far below their nominal capacity (Figure I-2(a)). Moreover, the reliability and efficiency of the electrical system cannot be ensured. So without energy storage systems, they are not dispatchable and by electrical system operators because their active and reactive power outputs are not controllable. Therefore, they cannot provide AS to the electrical grid.
On the contrary, the conventional power plants are controllable and can supply required powers to satisfy the grid requirements. They are considered as active generators and they can usually provide some AS: basically frequency regulation by active power control, voltage regulation by reactive power control, etc. They are mostly fossil and nuclear fueled and rely on the abundant fuel supply like coal, oil, natural gas or nuclear fuels. In fact, they can work at any power level below its nominal power by controlling the fuel supply (Figure I-2(b)). Therefore, passive RES cannot provide AS to the grid. A grid system operator cannot dispatch them because their outputs (active and reactive powers) are not controllable.
Integration of the Decentralized Production into Electrical Grids
From a centralized network to a decentralized network
The conventional electrical network organizations produce electrical power with centralized power units, such as nuclear, thermal or hydraulic power stations. In this centralized network structure, the distribution networks only host consumers and the power flows transit from high voltage levels towards the low voltage points. Thus, it only enables system operators to adjust the voltage level with a single power flow direction. The AS is provided on the transport network level by the production groups connected to it [2].
However, those centralized networks developed in the twentieth century have changed and generally evolved with decentralized power units, which are not dispatched in a centralized manner and generally connected to the distribution network (power does not exceed 50 to 100 MVA). In contrast to conventional production, the decentralized production is scattered over a territory and settled close to the power consumption facilities. They are especially favored by the development of RES. In many European countries, decentralized productions are based on cogeneration units, RES systems, and independent installed conventional producers. In this way, they can contribute to mitigate or solve technical, economic and environmental problems
[3]. Here are some reasons of favoring the decentralized production systems [4]:
the desire to reduce greenhouse gas emissions and thus encourages RES development;
the use of cogeneration systems increases the energy efficiency;
the opening up of the electricity market enables the independent producers emergence;
the widen of the energy supply range decreases the fossil fuel energy dependence in the European Union;
shorter construction periods and lower investments, compared to the conventional power units;
a power production close to the consumption leads to a transportation costs reduction.
Moreover, the AS provided by the distribution generators enables the system operators to keep the electrical quantities (for example, frequency and voltage) in an appropriate range. However, the intermittent nature of the RES makes the management of the distributed electrical network more difficult [5]. There are two basic parameters, which must be considered: voltage control and frequency control.
The connection of a generation unit will change the voltage, especially around the connection point. For example, when a PV generator is placed at the end of a transmission line, the voltage along the line will be increased in comparison to the situation without PV generator. A similar result is obtained when the PV generator is replaced by a conventional diesel generator [6]. In order to be able to contribute to the voltage adjustment by supplying or absorbing reactive and real power, the decentralized production units must be controllable.
The sudden consumption variations can induce network frequency fluctuations. The fast variations of the RES have the same effect as load variations. However, as long as the penetration rate remains low, this influence can be considered negligible. Recently, wind turbines are participating in the primary frequency control to ensure the network stability. In fact, when the power generation is more than consumption and thus the frequency is higher than the reference value (e.g. 50 Hz in France), wind turbines might be requested to reduce their production [7].
Perspectives for better integration of RES into electrical networks Limitations of the penetration level
The major problem of the decentralized RES integration into electrical networks is that they are not participating in AS, such as voltage and frequency control. As they are “passive” generators at the point view of system operators, voltage and frequency control is implemented by conventional generators that are equipped with alternators and turbines. Therefore, in order to guarantee the electrical network stability, the penetration level of such decentralized RES must be limited. For example, nowadays, the non-hydro RES penetration level is limited to 30 % of the power consumed in the French island electrical networks [4].
Decentralized power generation is sensitive to network disturbances, such as voltage drop or frequency deviation. It often leads to a disconnection of the power production from the network, or even aggravates the situation by a snowball effect. To avoid the worst situation, production facilities are requested to remain connected for limited periods of time in case of a voltage drop or frequency variation, according to the constraints that vary from an operator to another.
Production is another challenge that system operators need to deal with because of forecasting errors and uncertainties. For wind power production, 24-hour-ahead predictions are normally satisfying with 10 % of average uncertainty [4]. Even if the prediction accuracy is continually improving, controllable OR is required to compensate the forecasting uncertainties.
The capacity of electricity transport lines and power stations plays an important role. In the case of RES, such as wind power, PV power and hydraulic power, the production sites are sometimes far away from the consumption sites or the connection stations. Therefore, when the decentralized power production increases, new lines will be needed to prevent the transport lines congestion.
Perspectives for better integration into the networks
As presented in [4], three levels of evolution will be required for a significant increasing of distributed RES: the source level, the network level, and the consumer level.
At the source level, despite the intermittence nature and the forecasting uncertainties of the RES, it would be possible to increase the penetration level if the RES can operate in islanding mode, participate in the network management, and have an increased reliability. In order to fulfill these objectives, some advanced control technologies can be applied, such as
developing new control strategies for power electronic power inverters,
using energy storage systems for short and long term in order to be able to provide AS,
developing multi-source systems with an integrated energy management system, and so on.
At the network level, the congestion management is essential to guarantee electrical system security. Corrective measures for preventing unexpected congestions are the modification of the network topology or the modification of the production plans of the generation units.
Moreover, new network architectures, such as smart grids and microgrids, are able to increase efficiency and security of electric networks. By adopting the new communication technologies associated with advanced energy management systems, the electrical network improves its intelligence level and, therefore, increases the potential ability of integrating more RES.
In order to ensure an available and stable power level from RES, different kinds of energy storage devices are wildly used to compensate the random variations of the power output.
Table of contents :
General Introduction
Chapter.I Integration of Renewable Energy Sources in Electrical System and Management Issues
I.1. General Introduction
I.2. Renewable Energy Sources (RES)
I.2.1. Introduction
I.2.2. The Issue of RES: Benefits
I.2.3. Constraints and limitations
I.3. Integration of the Decentralized Production into Electrical Grids
I.3.1. From a centralized network to a decentralized network
I.3.2. Perspectives for better integration of RES into electrical networks
I.3.3. RES Integration Impacts in Electricity Markets
I.4. Smart City, Smart Grid and Microgrid
I.4.1. Smart city
I.4.2. Smart grid
I.4.3. Microgrid
I.5. Electrical Energy Storage
I.5.1. Applications and Services
I.5.2. Energy Storage Forms
I.5.3. Long Term Energy Provision and Fast Dynamic Power Capability
I.6. Hybrid Active Generators
I.6.1. Interest
I.6.2. Configuration of an active PV generator
I.7. Micro Gas Turbine
I.8. Microgrid Management
I.8.1. General introduction
I.8.2. Control functions and management with a communication system
I.8.3. Energy management system of a residential microgrid
I.9. Research Tasks, Method, and Content
References
Chapter.II Uncertainty Analysis and Forecasting of PV Power and Load Demand
II.1. General Introduction
II.2. Mathematical Modeling of PV Generator
II.3. PV Power Uncertainty Analysis
II.3.1. Introduction
II.3.2. Variability and Uncertainty of PV Power Output
II.3.3. Variability Indices for Irradiance and PV Power Output Variability Quantification
II.4. Load Demand Uncertainty Analysis
II.5. PV power and load forecast
II.5.1. Interest of power forecasting for power systems
II.5.2. Solar PV Power Generation Forecast
II.5.3. Load Demand Forecast
II.6. Forecasting with Back-Propagation ANN
II.7. Day-ahead PV Power and Load Forecast by Using ANN
II.7.1. Data Description
II.7.2. Error Computing Method
II.7.3. PV Power Forecast Application
II.7.4. Load Demand Forecast Application with ANN
II.8. Conclusion
References
Chapter.III Operating Reserve Quantification in a Microgrid by Considering the PV Power and the Load Forecasting Uncertainties
III.1. Introduction
III.2. Power Generation Reliability
III.2.1. Reliability background
III.2.2. Reliability evaluation techniques
III.2.3. System reliability indices
III.2.4. Frequency control and energy balancing
III.2.5. Frequency control with MGT
III.2.6. Voltage control and reactive reserve
III.3. Operating Reserve (OR)
III.3.1. Reserve Types
III.3.2. European vs North American Reserve Definitions
III.3.3. Calculation of OR by Considering Uncertainties from RES
III.4. Net Demand (ND) Uncertainty Analysis
III.4.1. Net Demand Forecasting
III.4.2. ND Uncertainty
III.5. Forecasting uncertainty assessment
III.6. Probabilistic Reliability Assessment
III.7. Risk-constrained OR Quantification for Each Interval
III.8. Conclusion
References
Chapter.IV Day-ahead Optimal OR Dispatching and Energy Management of a Microgrid System with Active PV Generators
IV.1. Introduction
IV.2. State of the Art on Power Reserve Dispatching
IV.3. Day-ahead Optimal Dispatching of the Reserve Power
IV.3.1. Introduction
IV.3.2. Procedures and Problems under Certain Non-linear Constraints
IV.4. OR Dispatching Strategies Considering the Maximization of RES Usage
IV.4.1 Uncertainty Analysis According to PV Power Production
IV.4.2 Scenario H
IV.4.3 Scenario L
IV.5. UC Problem Optimization with Dynamic Programming (DP)
IV.5.1. Formulation of the UC
IV.5.2. Optimizing Objective Functions with Different Optimization Strategies
IV.5.3. Dynamic Programming for the UC Problem
IV.6. A Case Study Application of the UCP with DP
IV.6.1. Presentation
IV.6.2. Different Scenarios of DP Application
IV.6.3. Comparison of the Power Reserve Dispatching
IV.6.4. Characteristics of PV based AG (with scenario H2)
IV.6.5. Security Level Analysis
IV.6.6. Analysis of the Cost
IV.7. OR Dispatching with Different PV Power Penetration Rate
IV.8. Conclusion
References
Chapter.V Development of an Energy Management System for an Urban Microgrid and Practical Application
V.1. Introduction
V.2. Analysis of the Urban Microgrid System
V.2.1. Presentation of the Case Study
V.2.2. Sources of Data in Utilities
V.2.3. Data Management for Uncertainty Analysis and Predictive Procedures
V.2.4. Urban Microgrid Management Analysis
V.3. Urban Microgrid EMS Framework and Interface Design
V.3.1. GUI Description
V.3.2. Data Collection and System Uncertainty Analysis
V.3.3. System Uncertainties Assessment and OR Quantification
V.3.4. Operational and OR Dispatching
V.4. Results and Discussion
V.5. Conclusion
References
General Conclusion
Appendix I. Renewable Energy Sources
A.I.1. Renewable energy coming directly from sun: Solar power
A.I.2. Renewable energy coming indirectly from sun
A.I.3. Renewable energy from other natural movements and mechanisms
Appendix II. Photovoltaic Circuit-based Physical Model
A.II.1. Equivalent Circuit Model
A.II.2. PV characteristic curves
A.II.3. PV cells connection (parallel and/or series)
A.II.4. Maximum power point tracker (MPPT)
Appendix III. Characteristics of MGTs
A.III.1. Efficiency characteristic
A.III.2. Estimation of exhaust gas emissions
A.III.3. Estimation of fuel consumption
A.III.4. Estimation of carbon dioxide emission
Appendix IV.Artificial Neural Network
A.IV.1. General Introduction
A.IV.2. Model Representation
A.IV.3. Feed-forward Procedure and Cost Function
A.IV.4. Back-Propagation (BP) Algorithm
A.IV.5. Learning parameters using fmincg
