SUMMARYIn this study, a large-scale dynamic optimization model (University of Regina Energy Model, UREM) has been developed for supporting long-term energy systems planning in the Region of Waterloo. The model can describe energy management systems as networks of a series of energy flows, transferring extracted/imported energy resources to end users through a variety of conversion and transmission technologies over a number of periods. It can successfully incorporate optimization models, scenario development and policy analysis within a general framework. Complexities in energy management systems can be systematically reflected; thus, the applicability of the modeling process can be highly enhanced. Four scenarios (including a reference case) are considered based on different energy management policies and sustainable development strategies for in-depth analysis of interactions existing among energy, socio-economy and environment in the Region. Useful solutions for the planning of energy management systems have been generated, reflecting trade-offs among energy-related, environmental and economic considerations. They are helpful for supporting (a) adjustment or justification of the existing allocation patterns of energy resources and services, (b) allocations of renewable energy resources, (c) formulation of local policies regarding energy consumption, economic development and energy structure, and (d) analysis of interactions among economic cost, system efficiency, emission mitigation and energy-supply security. Results also indicate that UREM can help tackle dynamic and interactive characteristics of the energy management system in the Region of Waterloo and can address issues concerning cost-effective allocation of energy resources and services. Thus, it can be used by decision makers as an effective technique in examining and visualizing impacts of energy and environmental policies, regional/community development strategies and emission reduction measures within an integrated and dynamic framework.
The installation of photovoltaic (PV) system for electrical power generation has gained a substantial interest in the power system for clean and green energy. However, having the intermittent characteristics of photovoltaic, its integration with the power system may cause certain uncertainties (voltage fluctuations, harmonics in output waveforms, etc.) leading towards reliability and stability issues. In PV systems, the power electronics play a significant role in energy harvesting and integration of grid-friendly power systems. Therefore, the reliability, efficiency, and cost-effectiveness of power converters are of main concern in the system design and are mainly dependent on the applied control strategy. This review article presents a comprehensive review on the grid-connected PV systems. A wide spectrum of different classifications and configurations of grid-connected inverters is presented. Different multi-level inverter topologies along with the modulation techniques are classified into many types and are elaborated in detail. Moreover, different control reference frames used in inverters are presented. In addition, different control strategies applied to inverters are discussed and a concise summary of the related literature review is presented in tabulated form. Finally, the scope of the research is briefly discussed.
Residential users (RUs) are the vital component of terminal energy consumption. The development and application of integrated energy system (IES) and smart homes has promoted RUs to actively take part in the trading with multi-energy provider (MEP) for its preferential energy prices and services. This paper proposes a pricing strategy of MEP by using a Stackelberg game-based bi-level programming model. In the upper level model, the adjustment coefficient of electric power price is optimized by MEP to increase the trading probability with RUs. In the lower level model, an integrated demand response (IDR) program is proposed for RUs to optimize the flexible loads in home energy management system (HEMS). Specially, a HEMS is composed of a smart interactive terminal, a micro combined cooling, heating, and power (mCCHP) system and multi-energy loads. Case study shows that, on one hand, the energy optimization based on IDR can help RUs manage their multi-energy loads and reduce the expected energy cost. On the other hand, the proposed price strategy of MEP can increase the trading probability, which promote more RUs to trade with MEP, thus increasing the MEP's benefit by 12.29%. The research proves that the proposed strategy is a win-win strategy and it is efficient in the pre-decisionmaking progress for MEP in the energy trading market.INDEX TERMS Integrated demand response (IDR), residential user (RU), multi-energy provider (MEP), Stackelberg game, pricing strategy, bi-level programing
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