Energy Systems Planning for Government Regulations
The energy networks, including power systems and natural gas systems, are in continuous profound evolution. During the last decade, this transformation turned into smart grids revolution. Many new technologies have appeared and they potentially will be integrated in the near future. The technologies also lead to significant progress in new theoretical engineering methods and new potential algorithms for optimization, control, and planning of the energy infrastructures. Those new algorithms are now data-enabled by better observability through state-of-the-art solutions. However, there is a vast need for new and better mathematical algorithms for exploiting the potential of these technologies in energy systems.
Furthermore, energy infrastructures are generally regulated as vital for the security of the countries. An unfortunate drawback of this tight control of the critical energy infrastructures is the monopoly often resulting in the lack of incentives to incorporate modern technologies and theoretical engineering solutions into practice. To mitigate the negative effect of the tight regulations of the critical energy infrastructures, countries are investing in research providing critical guidance to the government on updating or changing the regulations.
A set of electricity and gas network planning procedures and computational tools will be developed to implement robust planning based on new mathematical models and advance probabilistic modeling programming. The planning tools and procedures will allow the development of generalized guides of procedures, policies and grid codes specific for the grids of the future.
Data Analytics for Low-dimensional Models in Electric Power Grids
Presenting complex data structures with simpler data is a very important task. Simplified data is easier to work with and easier to visualize and analyze. In particular, data simplification plays an important role in real-time decision-making.
In power networks where decision-making is important at all stages of the work from long-term planning to finding the optimal real-time flow, understanding the data structure helps to make decisions more efficiently. One way to simplify the data and identify the structure in them is clustering. Identification of clusters in networks is necessary because of the increasing complexity of networks and the development of smart networks, for the implementation and effective operation of distributed optimization.
The most popular approach for decision-making under uncertainty is the so-called two-stage stochastic optimization (TSO), which considers scenarios relying on a priori definition of uncertainty probability distribution functions (PDFs). However, parameter PDFs are considered difficult to estimate, especially in situations where the system structure and market may experience deep changes. Conversely, adaptive robust optimization (ARO) approaches are agnostic to the parameter PDFs. ARO problem uncertainties are represented by uncertainty sets, generally defined through box-like limits and budget constraints. Within this setting, the solution to the ARO problem is that which performs the best under worst-case uncertainty realization. Thus, this framework can consider a wide range of plausible future paths and related uncertainty parameters. Under mild assumptions regarding the uncertainty nature and operational constraints, a tractable ARO mixed-integer linear programming (MILP) problem may be formulated. However, classical ARO approaches are not specifically designed to exploit existing partial information related to the uncertainty. Moreover, owing to the inherent conservativeness of ARO, expensive worst-case solutions may arise.
As an alternative, the proposed modeling approach relies on the distributionally robust optimization (DRO) framework, which considers all possible distributions defined within an ambiguity set. DRO concepts were first introduced in by Scarf, and in recent years, have been developed within a broad mathematical and operations research context. This framework is based on the worst-case expected total cost instead of the worst-case scenario. Therefore, the optimal solutions are such that the expected cost will not exceed the optimized value for all probability distributions within the ambiguity set, which is the set of considered distributions complying with the available partial information.
Cross-border Power Interconnection Stability, Benefits and Costs Allocation
Interconnection of power systems brings similar benefits as global economic cooperation. If a country can produce power in a cheaper way than its neighbors, it may consider exporting that power. However, there are many factors influencing this potential exchange. Naturally, economic benefits are one of them. But also the stability of the cooperation is a key factor for ensuring the practical feasibility of the interconnections. This is especially crucial in the case of several countries (e.g., Northeast Asian countries or South American countries), where mutual trust has not necessarily been strongly built in the past.
Any cooperation must be economically efficient to take place, even though some participants may eventually incur losses. But there are also other benefits of power interconnections, such as reliability, production efficiency, and controllability of power supply, which are especially important under a massive renewable-energy integration.
The importance of international energy cooperation is included in United Nations sustainable development goals:
“By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency and advanced and cleaner fossil-fuel technology, and promote investment in energy infrastructure and clean energy technology” (UN, 2018).
China is also highlighting the importance of cross-border interconnections, through the Global Energy Interconnection Initiative, which is proposed and analyzed by the Chinese Grid Company and Global Energy Interconnection Development and Cooperation Organization (GEIDCO). In Europe, the European Network of Transmission System Operators for Electricity (ENTSOE) was established to coordinate more than 40 regional transmission system operators, delivering power from one country to another according to economic signals.
The transportation sector represents more than 25% of the world’s energy consumption. It contributes to a large share of greenhouse emissions, e.g., in California, it accounts for more than 40% of emissions. Energy decarbonization roadmaps highlight the electrification of the transport sector as a critical step to reach emission goals. Likewise, emissions reductions are especially necessary for polluted mega-cities, where an increasing number of projects have replaced combustion-based bus fleets for electric ones; either entirely, like Shenzhen (China) or partially, Santiago de Chile (Chile).
It is anticipated that in less than a decade, there will be a significant presence of electric vehicles used for commercial transportation services. This hypothesis is supported by an increasing number of zero-emission-vehicle policies made by governments across the world. Notable examples include California with a mandate to have 5 million electric vehicles by 2030 and British Columbia requiring a 100% clean vehicle sales by 2040.
There is the potential to increase the number of electric passenger cars from just over 2 million in 2016 to 200 million in 2030. Electric buses and light-duty vehicles could number well over 10 million by 2030.
IRENA – International Renewable Energy Agency (ELECTRIC VEHICLES TECHNOLOGY BRIEF)
The impact in the distribution power grids will be enormous. Coordination of electric vehicle charging will be necessary. On the other hand, transport electrification has huge potential benefits for providing new services to the power grid resulting in new business models.
The need for secure and flexible operation of distributed power systems and the decline in prices for batteries have made energy storage deployment a viable option in many cases. The electric energy storage units’ characterization (including Li-ion batteries) currently utilized for power system operation and planning models relies on two major assumptions: the charge and discharge efficiencies are constant during such processes, and the maximum charge and discharge powers are independent of the system’s state of charge. This approach can lead to a misestimation of the storage available power and energy. In some of our research, we have observed up to 12% of the energy mismatch between the schedule when using an ideal battery model and true models.
We work with new alternative models for optimally manage energy storage systems connected to electric grids. The new models provide a characterization of the energy storage system performance including, non-linear charging and discharging efficiencies, as well as power limits for its charge and discharge as a function of the state of charge and requested power. It is possible to derive a linear reformulation of the optimization problem without the introduction of binary variables, allowing a computationally efficient model despite the higher accuracy. The proposed energy storage mathematical model provides a more accurate characterization of the system performance and technical operational limits with regard to the classical ideal models.