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The maturation of distributed energy resources (DER) has prompted the exploration of their deployment in commercial building applications due to their potential to supply energy at lower costs and emissions rates compared to centralized generation. While several software tools exist for evaluating the techno-economic potential of integrated renewable energy and combined heat and power (CHP) systems for distributed generation applications, many suffer from poor accuracy in capturing off-design (part load and changes in ambient air temperature and pressure) performance characteristics of microturbines, combustion turbines, or internal combustion engines. Thus, this paper presents a methodology for integrating these off-design characteristics in the mixed-integer linear program within REopt, a hybrid DER screening tool. The economic impact of the CHP off-design performance is observed through several application studies of various hybrid system configurations in different climates. Each study indicates how CHP off-design performance influences optimal sizing and dispatch decisions and therefore overall system economic value. We observe through case studies that modeling without the off-design effects, depending on the CHP prime mover and site, can result in Net Present Value predictions of hybrid systems that can be overoptimistic in frequently hot climates (up to 52 %), too conservative in frequently cold climates (up to 11 %), or unaffected (＋/−1 %) in temperate climates. Cases also highlight several advantages of hybrid systems relative to non-hybrid systems such as total economic value and the systems' ability to mitigate potentially negative consequences attributed to off-design performance. • A methodology for off-design performance as a function of load and ambient conditions is presented. • Scenarios where different application climates affect optimal sizing and dispatch are provided. • Not including off-design CHP modeling can result in overly optimistic or conservative economics. • Higher economic values for hybrid systems compared to either renewable energy or CHP-only systems. [ABSTRACT FROM AUTHOR]

The concept of off-grid hybrid wind energy system is financially attractive and more reliable than stand-alone power systems since it is based on more than one electricity generation source. One of the most expensive components in a stand-alone wind-power system is the energy storage system as very often it is oversized to increase system autonomy. In this work, we consider a hybrid system which consists of wind turbines, photovoltaic panels, diesel generator and battery storage. One of the main challenges experienced by project managers is the sizing of components for different sites. This challenge is due to the variability of the renewable energy resource and the load demand for different sites. This paper introduces a sizing model that has been developed and implemented as a graphical user interface, which predicts the optimum configuration of a hybrid system. In particular, this paper focuses on seeking the optimal size of the batteries and the diesel generator usage. Both of these components are seen to be trade-offs from each other. The model simulates real time operation of the hybrid system, using the annual measured hourly wind speed and solar irradiation. The benefit of using time series approach is that it reflects a more realistic situation; here, the peaks and troughs of the renewable energy resource are a central part of the sizing model. Finally, load sensitivity and hybrid system performance analysis are demonstrated. [ABSTRACT FROM AUTHOR]

Photovoltaic and wind power generations are currently seen as sustainable options of in rural electrification, particularly in standalone applications. However the variable character of solar and wind resources as well as the variable load demand prevent these generation systems from being totally reliable without suitable energy storage system. Several research works have been conducted on the use of photovoltaic and wind systems in rural electrification; however most of these works have not considered other ways of storing energy except for conventional battery storage systems. In this paper, an energy dispatch model that satisfies the load demand, taking into account the intermittent nature of the solar and wind energy sources and variations in demand, is presented for a hybrid system consisting of a photovoltaic unit, a wind unit, a pumped hydro storage system and a diesel generator. The main purpose of the developed model is to minimize the hybrid system’s operation cost while optimizing the system’s power flow considering the different component’s operational constraints. The simulations have been performed using “fmincon” implemented in Matlab. The model have been applied to two test examples; the simulation results are analyzed and compared to the case where the diesel generator is used alone to supply the given load demand. The results show that using the developed control model for the proposed hybrid system, fuel saving can be achieved compared to the case where the diesel is used alone to supply the same load patters. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods. [ABSTRACT FROM AUTHOR]

• Mixed integer non-linear programming was used for optimization of system operation. • Economic benefits could be obtained by MPC compared to RBC and ON/OFF strategies. • Combine TES, solar PV, and MPC technologies improved the overall system yield. • Different climate conditions upon a MPC strategy were studied in a heating system. Thermal energy storage (TES) systems allow the user to store energy produced from renewable energy sources during lower consumption periods, and to consume it when the energy demand is higher. Moreover, the use of TES systems encourages the user to take advantage of the time-of-use (ToU) tariff structures in the electricity market. Framed in this goal, the present paper evaluated the economic impact of a novel model predictive control (MPC) strategy that employed an inner control algorithm (ICA) to contribute in the cost electricity reduction of 17 different climate zones while the accuracy in results was improved and the computational effort was maintained. The studied system was composed of an air-to-water heat pump (HP), photovoltaic (PV) panels, and a water TES tank. Its response was based on forecasting the weather conditions, grid electricity price, and heating demand as a dynamic controller. Its economic benefits were compared against a rule-based control (RBC) method and an ON/OFF strategy. The results showed that a good operation of the system was strongly linked with proper sizing of the water TES tank and PV panels, especially in the coldest zones. Nevertheless, using the proposed MPC strategy with its ICA, the system was able to reduce the energy cost between 16% and 22% in most heating demanding climates compared to the RBC strategy and between 17% and 46% compared to the ON/OFF one. Moreover, the energy purchased from the electricity grid during on-peak periods decreased dramatically with the MPC strategy, reaching around 90% reduction in almost all studied climates. [ABSTRACT FROM AUTHOR]