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• Analyzing various architectural function ratio combinations of urban complex. • Evaluating energy performance of urban complex with various function combinations. • Providing function scheme data set of urban complex in terms of energy performance. • Improving pre-programming process of urban complex by considering energy factor. In the context of the global energy shortage, urban complexes have attracted more and more attention from energy-saving research due to their large number and great energy-saving potential. While, the energy performance factors are rarely considered in the function scheme decision of urban complex in the recent pre-programming process. Optimizing the building area ratio scheme of urban complex is the key link to improve the comprehensive energy utilization efficiency. This paper proposes an approach to optimize building area ratio scheme of urban complex from energy performance perspective by energy simulation, scenario analysis and demand side management methods, and provides a data set of rankings of urban complex function combination schemes by evaluating the performance of energy load leveling and the degree of load stability of each building area ratio scheme in different climatic conditions, respectively. The results indicate that: 1) the different building area ratios notably affect the integrated operating energy performance of urban complexes with a maximum evaluation indexes difference of more than 85%; 2) the evaluating and optimizing results of the building area ratio schemes show broadly similarity in different climate conditions; 3) the optimal building area ratio scheme of urban complex is usually with less "shopping mall" (0 ∼ 10%), more "residence" (more than 35%), and floating ratio of "office" and "hotel" (0 ∼ 35%). The main findings of this study provide a quantitative basis for the architectural pre-programming and function scheme decision-making of urban complex by demand-side energy management to improve the energy efficiency, so as to guarantee the energy supply security. [ABSTRACT FROM AUTHOR]

• Detailed energy consumption models for different appliance categories are developed. • Modular models can be used as building blocks enhancing their flexibility. • MILP models of appliance operation can be incorporated in energy management systems. • Models allow for the integrated optimization of all energy resources. • Models enable the participation in demand response programs. Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf. [ABSTRACT FROM AUTHOR]

e-harbours is a unique European project that was set out to identify viable energy business cases on the exploitation of energy flexibility, which optimise their operations to match energy demand and supply while taking account of the additional volatility in supply caused by renewable energy sources, improve energy efficiency, and reduce dependence on fossil fuels. In this paper, we propose an integrated multi-criteria decision analysis based framework to assess the relative performance of 21 energy business cases, which implemented different demand-side management strategies. Our proposed methodology has the ability to address complex problems involving multiple conflicting interests from various stakeholders, different forms of data, and different fuzzy and crisp relations. We find that business cases based on contract optimisation and offering reserve capacity were ranked relatively high, while those based on trading on the wholesale market or hybrid approaches fared less well. Despite finding viable pilot business cases, e-harbours found that there was little enthusiasm among industrial partners to scale up the pilots. Consequently, EU governments should consider offering attractive incentive programmes for industry engagement in achieving their objectives in reducing greenhouse gas emissions, improving energy supply security, diversifying energy supplies, and improving Europe’s industrial competitiveness. [ABSTRACT FROM AUTHOR]