Journal of Energy Management and Technology
Volume:1 Issue: 3, Autumn 2017

In recent years, Short-term load and price forecast has always been a key issue for power system operation. In regulated power systems, Short-term load forecast is an important tool for reliable and economic operation of power systems. Many operating decisions are based on Short-term load forecasting, such as dispatch scheduling of generating capacity, reliability analysis, security assessment and maintenance plan for the generators. On the other hand, electricity price variation are more important and effective factors for all power markets participants. Bidding strategy, risk control, investment decisions, demand and supply balancing and power system reliability and other power markets applications are highly depended to load and price uncertainty. In this paper a new intelligent hybrid method has been proposed to price and load uncertainty prediction. The proposed method consists of an improved version of particle swarm optimization algorithm to fine tuning the main predictor system’s adjustable parameters. The price and load variation intervals have been predicted by predictor system based on multi-layer neural networks. The proposed method has been examined in some well-known power markets.

In this study, a focus on geometry & elements is taking place with open expansion tanks (OETs) to reduce the energy loss in the system. A new model based on the inlet and outlet flow control in the tank is developed by a semi-spiral coil. A fully numerical comparison of the proposed new OET and the conventional system is done in assistance with Computational Fluid Dynamic (CFD) modeling that has been utilized by QuickerSim CFD toolbox. In order to approve the data, an experimental set up was installed in the laboratory, which includes: three types of OETs (conventional, optimized twin container, and optimized by the coil). Finally, the economic analysis of the new design tank has been performed. The Net Present Value (NPV) in all the three scenarios (international, without subsidy and with subsidy) for optimized models is more than zero and Internal Rate of Return (IRR) is concluded 38%, 113.3% & 303%, respectively. Furthermore, the value of the Simple Payback (SPB) is half a year accordingly in the international energy price calculation based scenario. The results demonstrate the proposed OET that uses a coil, avoids the energy losses as much as possible. As an overall achievement, the energy losses in the OET with the coil are reduced in the range of 80% to 95%. Additionally, replacement of the conventional models by the new proposed design results in the annual energy saving of 857.1 cubic meters of natural gas per household.

In recent years, wind power generation is rapidly gaining popularity due to the major concerns about the excessive emissions and global energy crisis. In addition, this kind of power systems have shown more security options than others. Due to the highly variable and intermittent nature of the wind energy, it is crucial to achieve higher accuracy of longterm wind speed forecasts for improving the reliability and economic feasibility of the power systems. The forecasting is the best standard for comparing the certitude of algorithm with current analytical methods. By importing the intelligent algorithms, we can overcome the obstacles of prediction and eliminate the volume of Calculation which are the main problems of determining the uncertainty nature of such renewable energy systems. Hence, this paper proposes a novel methodology for long-term wind speed forecasting using back propagation artificial neural network. The neural networks are powerful tools for solving the complex problems and providing tolerable standpoint from distributed energies. Simulation result illuminates that the proposed algorithm can offer highly features of compatibility and accuracy for wind predictions in comparison with actual wind speed reports of Iran meteorological organization.

Combined heat and power (CHP) systems have been utilized more and more in power systems, recently. With the increasing penetration of CHP-based co-generation of electricity and heat, determination of economic dispatch of power and heat becomes a more complex and challenging task. In this paper, the optimal operation of CHP-based system is studied and an algorithm is proposed for solution of it. The optimal operation of CHP-based systems or CHP economic dispatch is inherently a nonlinear and non-convex optimization problem with a lot of local optimal solutions. In this paper, frog leaping algorithm is used for solution of the problem. This heuristic algorithm is well capable to attain the optimal solutions even in the case of non-convex optimization problems. The proposed method is implemented on several standard test systems. The obtained results have been compared with other intelligent search algorithms. The numerical simulations verify that optimal operation of CHP systems can result in a large economic annual saving.

In this paper, we proposed a financial evaluation method for energy centers with the ability to convert and manage the selection of the type of exhaust fuels. An energy center is an integrated system, for example, a power unit that convert or store multiple energy carriers. Given the flexibility of energy centers to change their output power (s), as well as the uncertainties in some parameters such as the price of energy carriers, inflation, consumption, etc., the value of its investment is uncertain and based on The Monte Carlo simulation is determined. By the Monte Carlo method and the Hull-White-Vasicek method, the price of energy carriers will be estimated in the coming days using data from previous years. This approach has been the ability to adapt flexibly to uncertain and volatile market prices. The estimated price error is shown using the above methods and real data. Then, the model of the proposed energy center is determined by the size of the risk and its profitability.

Solar thermal energy is a renewable, convenient source and environmentally benign energy resource. Flat-plate solar collectors are the most common and cost-effective devices for exploiting and converting solar energy into heat and transfer the heat to a medium. However, the efficiency of these systems is not favorable due to the poor thermo-physical properties of working fluid. Using nano-fluid is proposed as an efficient method in order to improve the heat transfer properties of the working fluid. Adding nanoparticles to base fluid, leads to enhancement in thermal properties of working fluid. Thermo-physical properties of base fluid depend on several parameters including particle concentration and size. In this study, the effect of these parameters is theoretically investigated on the thermal performance of a flat-plate solar collector. In addition, the collector efficiency is evaluated for different shapes of the cross-section of the riser pipe. It is observed that increase in nanoparticle volume fraction, enhances the efficiency of a flat-plate collector and the maximum obtained value was approximately 80%. Reversely, particle size increase from 20 nm to 80 nm, causes more than 3% reduction in the efficiency. In addition, for different shapes of the pipe cross-section, including circle, square, and triangle; circular cross sections leads to the highest efficiency in a flat-plate solar collector

Nowadays increasing in nonlinear loads intensifies harmonic problems and voltage distortion in distribution systems. With appropriate control of Distributed Generation (DG) resources in a Micro Grid (MG), it is possible to enhance the MG power quality. This paper proposes a hierarchical control method for Inverter Based Distributed Generators (IBDGs) to compensating voltage distortions such as: voltage harmonics, voltage unbalance and voltage sag and swell in Sensitive Load Bus (SLB).This method consists of two control level of primary and secondary. The secondary control proposes a selective harmonic compensating method and voltage unbalance compensation appropriately according to DGs impact on harmonic resonances. Some buses have more participation in exciting of the MG resonance modes. Therefore, larger harmonic compensation factors are considered for the IBDGs that are near to these buses. For the other IBDGs, the voltage unbalance compensation factor is selected bigger. On this basis, the Participation Factor (PF) of buses in different resonances is identified by network modal impedance analysis then, the compensation share of DGs would be prioritized accordingly. The primary control level includes of voltage controller, current controller, virtual resistor and DGs load compensation block. Effectiveness of the proposed control scheme is demonstrated through simulation studies.