• Energy Research

The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem.

AbstractThe heat exchanger has been widely used in the energy and chemical industry and plays an irreplaceable role in the featured applications. The design of heat exchanger is a mixed integer complex optimization problem, where the efficient design significantly improves the efficiency and reduces the cost. Many intelligent methods have been developed for heat exchanger optimal design. In this paper, a novel variant of sine and cosine algorithm named EDOLSCA is proposed, enhanced by dynamic opposite learning algorithm and the elite strategy. The proposed method is tested in CEC2014 benchmark and proved to be of significant advantages over the original algorithm. The new algorithm is then validated in the plate-fin heat exchanger (PFHE) optimal design problem. The comparison results of the proposed algorithm and other algorithms prove that EDOLSCA also has demonstrated superiority in heat exchanger optimal design.

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

Abstract Technical challenges facing the development of battery economic charging for energy management arise from various contradictory objectives, immeasurable internal states, and hard constraints. Available solutions often resort to optimizing economic charging from the perspective of power grid, while few focus on the benefits of electric vehicle (EV) owners. This paper proposes a constrained multi-objective optimization framework to achieve economy-conscious battery charging management. Specifically, a coupled electrothermal-aging model is first applied to capture the nonlinear electrical, thermal, and aging dynamics of a lithium-ion battery with different timescales. Then an economic indicator considering the total charging cost caused by both the battery aging and electrical energy loss is formulated, based on a battery resale cost model and the Beijing peak-valley time-of-use (TOU) price. In addition, two crucial but contradictory objectives including the charging time and battery average temperature are also taken into account, along with hard constraints on the battery State of Charge (SOC), charging current, terminal voltage, and temperature. The complicated multi-objective charging problem is solved by using NSGA-II to optimize the multistage constant-current (MCC) profile. Sensitivities of the cut-off voltage, convection resistance, and ambient temperature are comparatively analysed via Pareto frontier. Numerical results of two extreme cases and several balanced charging cases demonstrate that feasible, economy-conscious battery charging management for various user demands can be achieved through suitable equilibrations among charging speed and thermal variations during charging.

The climate change is addressing unprecedented pressures on conventional power system regarding the significant fossil fuel consumptions and carbon emissions, which largely challenges the conventional power system operation. This paper proposes a novel dynamic non-dominated sorting multi-objective biogeography-based optimization (Dy-NSBBO) to solve multi-objective dynamic economic emission load dispatch considering the mass integration of plug-in electric vehicles (PEVs), namely MO-DEELDP problem. First, a real-world economic emission load dispatch considering PEVs charging is first formulated as a constrained dynamic multi-objective optimization problem. Then a new multi-objective BBO is proposed adopting the non-dominated solution sorting technique, change detection and memory-based selection strategies in the multi-objective BBO method to strengthen the dynamic optimization performance. The proposed Dy-NSBBO is applied to solve three different dynamic economic emission load dispatch cases integrating four plug-in electric vehicle charging scenarios respectively. Comprehensive analysis shows that the novel algorithm is promising to bring considerable economic and environmental benefits to the power system operators and provides competitive charging strategies for policy makers and PEVs aggregators.

Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed integer variables and multiple constraints typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit.

Environmental friendly renewable energy plays an indispensable role in energy industry development. Foreign direct investment (FDI) in advanced renewable energy technology spillover is promising to improve technological capability and promote China’s energy industry performance growth. In this paper, the impacts of FDI renewable energy technology spillover on China’s energy industry performance are analyzed based on theoretical and empirical studies. Firstly, three hypotheses are proposed to illustrate the relationships between FDI renewable energy technology spillover and three energy industry performances including economic, environmental, and innovative performances. To verify the hypotheses, techniques including factor analysis and data envelopment analysis (DEA) are employed to quantify the FDI renewable energy technology spillover and the energy industry performance of China, respectively. Furthermore, a panel data regression model is proposed to measure the impacts of FDI renewable energy technology spillover on China’s energy industry performance. Finally, energy industries of 30 different provinces in China based on the yearbook data from 2005 to 2011 are comparatively analyzed for evaluating the impacts through the empirical research. The results demonstrate that FDI renewable energy technology spillover has positive impacts on China’s energy industry performance. It can also be found that the technology spillover effects are more obvious in economic and technological developed regions. Finally, four suggestions are provided to enhance energy industry performance and promote renewable energy technology spillover in China.