Co-optimization of energy and reserve capacity considering renewable energy unit with uncertainty

This paper proposes a system model for optimal dispatch of the energy and reserve capacity considering uncertain load demand and unsteady power generation. This implicates uncertainty in managing the power demand along with the consideration of utility, user and environmental objectives. The model takes into consideration a day-ahead electricity market that involves the varying power demand bids and generates a required amount of energy in addition with reserve capacity. The lost opportunity cost is also considered and incorporated within the context of expected load not served. Then, the effects of combined and separate dispatching the energy and reserve are investigated. The nonlinear cost curves have been addressed by optimizing the objective function using robust optimization technique. Finally, various cases in accordance with underlying parameters have been considered in order to conduct and evaluate numerical results. Simulation results show the effectiveness of proposed scheduling model in terms of reduced cost and system stability

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

MCA4climate: A Practical Framework for Planning Pro-Development Climate Policy

MCA4climate is a major new UNEP initiative providing 1 Introduction practical assistance to governments in preparing their climate change mitigation and adaptation plans and strategies. It aims to help governments, particularly in developing countries, identify policies and measures that are low cost, environmentally effective and consistent with national development goals. It does this by providing a structured approach to assessing and prioritizing climate-policy options, while taking into consideration associated social, economic, environmental and institutional costs and benefits. In doing so, it seeks to counter the widely held perception that tackling climate change is costly, highlight the potential developmental benefits of addressing climate change and encourage action to that end

What's Blocking the Sun?: Solar Photovoltaics for the U.S. Commercial Market

Provides an overview of installation trends and investment climate for solar photovoltaics in the U.S. commercial sector, including policy and economic obstacles. Recommends strategies for the solar industry, the commercial sector, and policy makers

Topological changes in data-driven dynamic security assessment for power system control

The integration of renewable energy sources into the power system requires new operating paradigms. The higher uncertainty in generation and demand makes the operations much more dynamic than in the past. Novel operating approaches that consider these new dynamics are needed to operate the system close to its physical limits and fully utilise the existing grid assets. Otherwise, expensive investments in redundant grid infrastructure become necessary. This thesis reviews the key role of digitalisation in the shift toward a decarbonised and decentralised power system. Algorithms based on advanced data analytic techniques and machine learning are investigated to operate the system assets at the full capacity while continuously assessing and controlling security. The impact of topological changes on the performance of these data-driven approaches is studied and algorithms to mitigate this impact are proposed. The relevance of this study resides in the increasingly higher frequency of topological changes in modern power systems and in the need to improve the reliability of digitalised approaches against such changes to reduce the risks of relying on them. A novel physics-informed approach to select the most relevant variables (or features) to the dynamic security of the system is first proposed and then used in two different three-stages workflows. In the first workflow, the proposed feature selection approach allows to train classification models from machine learning (or classifiers) close to real-time operation improving their accuracy and robustness against uncertainty. In the second workflow, the selected features are used to define a new metric to detect high-impact topological changes and train new classifiers in response to such changes. Subsequently, the potential of corrective control for a dynamically secure operation is investigated. By using a neural network to learn the safety certificates for the post-fault system, the corrective control is combined with preventive control strategies to maintain the system security and at the same time reduce operational costs and carbon emissions. Finally, exemplary changes in assumptions for data-driven dynamic security assessment when moving from high inertia to low inertia systems are questioned, confirming that using machine learning based models will make significantly more sense in future systems. Future research directions in terms of data generation and model reliability of advanced digitalised approaches for dynamic security assessment and control are finally indicated.Open Acces

Optimal sizing and techno-economic analysis of grid-connected nanogrid for tropical climates of the savannah

Reliability and costs are mainly considered in performance analysis of renewable energy-based distributed grids. Hybrid Optimization of Multiple Energy Renewables was used in techno-economic analysis of renewable energy systems involving photovoltaics, wind, diesel and storage in tropical regions of Amazon, Central Asia and Mediterranean. In a study for a Guinea Savannah region, 70% of renewable energy fraction was achieved. However, levelized cost of energy of 0.689 $/kWh was higher than tariff rate of 0.6$/kWh. This paper considers Hybrid Optimization of Multiple Energy Renewables to achieve lower levelized cost of energy and net present costs of a nanogrid for increased reliability and low per capita energy consumption of 150 kWh in a Sudan Savannah region of Nigeria. The proposed grid connected nanogrid aims to serve daily residential demand of 355 kWh. A range of 0.0110 $/kWh to 0.0095$/kWh and $366,210 to$288,680 as negative values of levelized cost of energy and net present cost respectively were realized, implying potentials for a large grid export. The renewable energy fraction of up to 98% was also achieved in addition to low greenhouse gas emission of 2,328 tons/year. The results may further be consolidated with strategies for power dispatch and load scheduling

Integration of renewable energy into Nigerian power systems

Many countries are advancing down the road of electricity privatization, deregulation, and competition as a solution to their growing electricity demand and other challenges posed by the monopolistic nature of the existing structure. Presently, Nigeria has a supply deficit of electricity as a result of the growing demand. This imbalance has negatively affected the economy of the country and the social-economic well-being of the population. Hence, there is an urgent need to reform the power sector for greater efficiency and better performance. The objectives of the reform are to meet the growing power demand by increasing the electric power generation and also by increasing competitiveness through the participation of more private sector entities. The renewable energy integration is one way of increasing the electricity generation in the country in order to cater for the growing demand adequately. Examples of the renewable energy that is available in the country include wind, geothermal, solar and hydro. They are considered to be environmentally friendly, replenishable and do not contribute to the climate change phenomena. The country presently generates the bulk of its electricity from both thermal (85%) and hydroelectric (15%) power plants. While electricity generation from the thermal power stations constitutes the largest share of greenhouse emission, this is mostly from burning coal and natural gas. The effect of this high proportion of greenhouse emission causes climate change which is referred to as a variation in the climate system statistical properties over a long period of time. It has been observed that many of the activities of human beings are contributory factors to the release of these greenhouse gases (GHG). But, as the traditional sources of energy continue to threaten the present and future existence on the planet earth, it is, therefore, imperative to increase the integration of the variable renewable energy sources in a sustainable and eco-friendly manner over a long period of time. The variability and the uncertainties of the renewable energy source's output, present a major challenge in the design of an efficient electricity market in a deregulated environment. The system deregulation and the use of renewable sources for the generation of electricity are major changes presently being experienced in power system. In a deregulated power system, the integration of renewable generation and its penetration affects both the physical and the economic operations. The main focus of this research is on the integration of wind energy into Nigerian power systems. Up till now, research on the availability of the wind energy and its economic impacts has been limited in Nigeria. Generally, the previous study of wind energy availability in Nigeria has been limited in scope. The wind energy assessment study has not been detailed enough to be able to ascertain the wind energy potential of the country. To cope with this shortcoming, a detailed statistical wind modeling and forecasting methodology have been used in this thesis to determine the amount of extractable wind energy in six selected locations in Nigeria using historical wind speed data for 30 years. The accuracy test of the statistical models was also carried using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Chi-Square methods to determine the inherent error margin in the modeling and analysis. It is found that the error margin of the evaluations falls within the expected permissible tolerance range. For a more detailed wind assessment study of the Nigeria weather, the seasonal variation of the weather conditions as it affects the wind speed and availability during the two major seasons of dry and rainy was considered. A Self-Adaptive Differential Evolution (SADE) was used to solve the economic load dispatch problem that considers the valve-point effects and the transmission losses subject to many constraints. The results obtained were compared with those obtained using the "standard" Differential Evolution (DE), Genetic Algorithm (GA), and traditional Gradient Descent method. The results of the SADE obtained when compared with the GA, DE, and Gradient descent show the superiority of SADE over all the other methods. The research work shows that the wind energy is available in commercial quantity for generation of electricity in Nigeria. And, if tapped would help reduce the gap between the demand and supply of electricity in the country. It was also demonstrated that the wind energy integration into the power systems affects the generators total production cost

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems

Dynamic constrained coalition formation among electric vehicles

Background: The use of electric vehicles (EVs) and vehicle-to-grid (V2G) technologies have been advocated as an efficient way to reduce the intermittency of renewable energy sources in smart grids. However, operating on V2G sessions in a cost-effective way is not a trivial task for EVs. The formation of coalitions among EVs has been proposed to tackle this problem. Methods: In this paper we introduce Dynamic Constrained Coalition Formation (DCCF), which is a distributed heuristic-based method for constrained coalition structure generation (CSG) in dynamic environments. In our approach, coalitions are formed observing constraints imposed by the grid. To this end, EV agents negotiate the formation of feasible coalitions among themselves. Results: Based on experiments, we show that DCCF is efficient to provide good solutions in a fast way. DCCF provides solutions whose quality approaches 98% of the optimum. In dynamically changing scenarios, DCCF also shows good results, keeping the agents payoff stable along time. Conclusions: Essentially, DCCF’s main advantage over traditional CSG algorithms is that its computational effort is very lower. On the other hand, unlike traditional algorithms, DCCF is suitable only for constraint-based problems

A Review of Forecasting Algorithms and Energy Management Strategies for Microgrids

As an autonomous subsystem integrating with the utility, a microgrid system consists of distributed energy sources, power conversion circuits, storage units and adjustable loads. With the high penetration of distributed generators, it is challenging to provide a reliable, consistent power supply for local customers, because of the time-varying weather conditions and intermittent energy outputs in nature. Likewise, the electricity consumption changes due to the season effect and human behaviour in response to the changes in electricity tariff. Therefore, studies on accurate forecasting power generation and load demand are worthwhile in order to solve unit commitment and schedule the operation of energy storage devices. The paper firstly gives a brief introduction about microgrid and reviews forecasting algorithms for power supply side and load demand. Then, the mainstream energy management approaches applied to the microgrid, including centralized control, decentralized control and distributed control schemes are presented. A number of the optimal energy management algorithms are highlighted for centralized controllers based on short-term forecasting information and a generalized centralized control scheme is thus summarized. Consensus protocol is discussed in this paper to solve the cooperative problem under the multi-agent system-based distributed energy system. Finally, the future of energy forecasting approaches and energy management strategies are discussed