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Abstract To achieve optimal generation from a number of mixed power plants by minimizing the operational cost while meeting the electricity demand is a challenging optimization problem. When the system involves uncertain renewable energy, the problem has become harder with its operated generators may suffer a technical problem of ramp-rate violations during the periodic implementation in subsequent days. In this paper, a scenario-based dynamic economic dispatch model is proposed for periodically implementing its resources on successive days with uncertain wind speed and load demand. A set of scenarios is generated based on realistic data to characterize the random nature of load demand and wind forecast errors. In order to solve the uncertain dispatch problems, a self-adaptive differential evolution and real-coded genetic algorithm with a new heuristic are proposed. The heuristic is used to enhance the convergence rate by ensuring feasible load allocations for a given hour under the uncertain behavior of wind speed and load demand. The proposed frameworks are successfully applied to two deterministic and uncertain DED benchmarks, and their simulation results are compared with each other and state-of-the-art algorithms which reveal that the proposed method has merit in terms of solution quality and reliability.

Optimising industrial motor driven applications, when coupled with best practice motor management, can deliver more than 57 energy savings. The need for variable speed drives and their advantages are very well known, but unfortunately, the integration of the knowledge of efficient electrical motors and advantages of power electronics engineering is not well practiced by industries. It has also been observed that further saving can be achieved by integration of efficient systems. In this paper, a compact integral motor-drive unit built to operate in arduous industrial environments with optimised power consumption and best practice motor management under different load conditions is proposed. Reduction of the losses in the system provides higher efficiency and further savings to overcome heat without requirement of any additional cooling system. The critical issues such as space limitations within the existing spatial dimensions of motor, management of heat transfer from the motor to the sensitive drive control unit, management of vibration transfer to the sensitive drive control unit, management of electromagnetic interference within the unit and externally, management of other environmental effects on the sensitive drive control unit have been considered and discussed in this paper.

Abstract This paper is based on dynamic optimization methodology to investigate the economic energy efficiency issues in developing countries. The paper introduces some definitions about energy efficiency both in economics and physics, and establishes a quantitative way for measuring the economic energy efficiency. The linkage between economic energy efficiency, energy consumption and other macroeconomic variables is demonstrated primarily. Using the methodology of dynamic optimization, a maximum problem of economic energy efficiency over time, which is subjected to the extended Solow growth model and instantaneous investment rate, is modelled. In this model, the energy consumption is set as a control variable and the capital is regarded as a state variable. The analytic solutions can be derived and the diagrammatic analysis provides saddle-point equilibrium. A numerical simulation based on China is also presented; meanwhile, the optimal paths of investment and energy consumption can be drawn. The dynamic optimization encourages governments in developing countries to pursue higher economic energy efficiency by controlling the energy consumption and regulating the investment state as it can conserve energy without influencing the achievement of steady state in terms of Solow model. If that, a sustainable development will be achieved.

Abstract To guarantee the space heating in the heating season, conventional combined heat and power (CHP) plants operate in a heat-controlled operation mode, resulting in restricted peak-shaving ability (PSA). To improve the CHP plant’s PSA, a novel solar aided CHP (SA-CHP) system is proposed and simulated in this paper. In the new system, solar heat could be flexibly used to generate power or to supply heat according to the heating and power demands, thereby realizing the heat-power decoupling. A set of models for the SA-CHP system is developed and validated. The PSA, the standard coal consumption (SCC) and the techno-economic performances of a 330 MWe SA-CHP system are comprehensively analyzed in this paper. The results show that the SA-CHP system can significantly improve (up to double) the PSA compared with the CHP plant under the same rated heating power. The feasible operation region area of the SA-CHP system is 74.7% larger than that of the CHP plant. The annual SCC of the SA-CHP system are 17378.23 t less than that of the CHP plant. The net annual revenue of the SA-CHP system is $2.24 M. Besides, techno-economic performances of SA-CHP systems with two different heat storage systems are compared.

Abstract To understand the relationship between green building energy performance and regional commercial estates, this study analysed Australia’s Commercial Building Disclosure (CBD) program database. This database discloses the annual energy use intensity (EUI) and the corresponding energy rating (1–6 stars) of 2460 National Australian Built Environment Rating System (NABERS) certified office buildings. The study selected for analysis Australia’s six largest cities and then used panel data regression, where commercial estate factors (total stock of office buildings, vacancy rate, average gross face rent, and government incentives such as financial support) served as independent variables and the EUI was the dependent variable. The p-values of all the models are below 0.05, indicating that the results are statistically significant. Results showed the commercial real estate factors were significantly related to the EUI for buildings with a rating of 1 star and above. The correlation between EUI and commercial real estate factors became less strong with the rating level increasing. The effect of ‘green building’ branding makes the office buildings more attractive with regard to tenancy and their energy performance more reflective of the variation in the commercial real estate market. This study is a frontrunner in contextualising green building energy performance and ratings in the context of regional commercial estate, and the regression models employed in the study could be used to define regional baselines for energy ratings in future studies.

Abstract In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19 % to 57.39 % , depending on the actual cost ratio of the two types of errors.

Abstract Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

Abstract The ethylene cracking furnace system is crucial for an olefin plant. Multiple cracking furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.