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Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Current energy market designs and pricing schemes fail to give investors the appropriate market signals. In particular, energy prices are not high enough to attract investors to build new or maintain existing power capacity. In this paper we propose a method to compute second-best Pareto optimal equilibrium prices for any market exhibiting non-convexities and, based on this result, an energy market design able to restore the correct energy price signals for supply investors.

Direct Load Control (DLC) is an effective instrument for achieving a guaranteed load curtailment. Unfortunately, if a customer considers that personal discomfort outweighs money savings after DLC shutting down of home appliances, DLC solution can be rejected. This paper proposes the way to remediate customer comfort concerns by pre-storing additional energy in electric loads before their disconnection from the grid. We take electric tank water heaters as an illustrative example of residential loads with storage. To illuminate our approach we first show how to balance electric consumption for pre-storing with user thermal discomfort for a single water heater. Then, we illustrate how the approach can be scaled-up to a multiple-boiler scenario, when ten remotely controlled boilers act next to fifty non-controlled boilers. The simulations for the latter case show that the expected user thermal discomfort can be significantly reduced at the cost of a reasonable increase of electricity demand preceding DLC event.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.

The unpredictable nature of wind energy makes its integration to the electric grid highly challenging. However, these challenges can be addressed by incorporating storage devices (batteries) in the system. We perform an overall assessment of a single domestic power system with a wind turbine supported by an energy storage device. The aim is to investigate the best operation mode of the storage device such that the occurrence of large power spills can be minimized. For estimating the small probability of large power spills, we use the splitting technique for rare-event simulations. An appropriate Importance Function for splitting is formulated such that it reduces the work-load of the probability estimator as compared to the conventional Crude Monte Carlo probability estimator. Simulation results show that the ramp constraints imposed on the charging/discharging rate of the storage device plays a pivotal role in mitigating large power spills. It is observed that by employing a new charging strategy for the storage device large power spills can be minimized further. There exists a trade-off between reducing the large power spills versus reducing the average power spills.

Abstract Hybrid energy storage is a multi-modal approach to store and supply different forms of energy (electricity, heat, cold) simultaneously. This is an important sector coupling approach and enables large scale flexibility for a deep decarbonization of energy systems. Two different proposed energy storages – power-to-heat-to-X energy storage (PHXES) and pumped thermal energy storage (PTES) – are investigated in detail in this work towards their potential towards a hybrid deployment. The techno-economic analysis includes thermodynamic flowsheet simulations and unified cost estimations based on the used equipment. It is shown that the main input data set (e.g. working fluid selection, turbomachinery efficiency, pinch temperatures) has a strong influence on storage roundtrip efficiency in the thermodynamic simulations, but also on storage costs. Cost decrease and performance increase can be conflicting development targets, and it is not clear if future energy systems rather require technologies with the highest storage efficiencies or if low cost is an important prerequisite. PHXES and three variants of PTES are implemented into a simplified energy system model of the city of Hamburg that comprises the electric load and heat demand of the district heating system. Two scenarios (fossil benchmark and 80% decarbonization) are used to select the technologies and their capacities that allow the most cost-effective energy supply solution by a linear-programming optimization procedure. Large scale renewable power generation and hybrid energy storage play the major roles in the decarbonization scenario. PHXES with a storage capacity of 69 GWh, hot water storage (3.6 GWh) and a battery system (430 MWh) are part of the cost-optimal solution. A cost sensitivity analysis shows that a total cost reduction of 60–80% is required for PTES to become competitive in this model scenario.

Mixed transition metal oxides with high theoretical capacity show great potential to replace carbonaceous anode materials in lithium-ion batteries.

Bioplastics are alternatives of conventional petroleum-based plastics. Bioplastics are polymers processed from renewable sources and are biodegradable. This study aims at conducting an environmental impact assessment of the bioprocessing of agricultural wastes into bioplastics compared to petro-plastics using an LCA approach. Bioplastics were produced from potato peels in laboratory. In a biochemical reaction under heating, starch was extracted from peels and glycerin, vinegar and water were added with a range of different ratios, which resulted in producing different samples of bio-based plastics. Nevertheless, the environmental impact of the bioplastics production process was evaluated and compared to petro-plastics. A life cycle analysis of bioplastics produced in laboratory and petro-plastics was conducted. The results are presented in the form of global warming potential, and other environmental impacts including acidification potential, eutrophication potential, freshwater ecotoxicity potential, human toxicity potential, and ozone layer depletion of producing bioplastics are compared to petro-plastics. The results show that the greenhouse gases (GHG) emissions, through the different experiments to produce bioplastics, range between 0.354 and 0.623 kg CO2 eq. per kg bioplastic compared to 2.37 kg CO2 eq. per kg polypropylene as a petro-plastic. The results also showed that there are no significant potential effects for the bioplastics produced from potato peels on different environmental impacts in comparison with poly-β-hydroxybutyric acid and polypropylene. Thus, the bioplastics produced from agricultural wastes can be manufactured in industrial scale to reduce the dependence on petroleum-based plastics. This in turn will mitigate GHG emissions and reduce the negative environmental impacts on climate change.