Grid Electricity Price Research Articles

Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model

To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.

An Integration of Adaptive Control Techniques with Portable Solar-Powered EV Charging with Particle Swarm Optimization (PSO)

Abstract: This paper presents a novel approach utilizing Particle Swarm Optimization (PSO) for enhancing the efficiency of solar-powered electric vehicle (EV) charging systems. With the increasing adoption of EVs and the imperative to transition towards renewable energy sources, optimizing the utilization of solar power for charging becomes crucial. The proposed system integrates photovoltaic (PV) panels to harness solar energy, providing a sustainable and renewable power source for EV charging. The key innovation lies in the application of PSO, a nature-inspired optimization algorithm, to dynamically adjust charging parameters based on real-time environmental conditions, grid electricity prices, and EV battery requirements. PSO offers a robust and efficient method for solving optimization problems by simulating the behavior of swarms in nature. In the context of solar-powered EV charging, the PSO algorithm iteratively adjusts charging rates and schedules to maximize solar energy utilization, minimize charging costs, and ensure optimal battery health. The integration of PSO optimization with solarpowered EV charging represents a promising step towards achieving sustainable and intelligent transportation solutions. Future research directions may include further optimization refinements, scalability studies, and real-world deployment to validate the practicality and effectiveness of the proposed approach in diverse environments and usage scenarios.

Parameter Design of a Photovoltaic Storage Battery Integrated System for Detached Houses Based on Nondominated Sorting Genetic Algorithm-II

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building’s energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house.

Operation optimization of cogeneration microgrid based on deep reinforcement learning

Abstract The deep Q learning method is adopted to transform the microgrid model into the Markov decision model to realize the coordinated management and energy optimization of the microgrid. For the problem of the Markov decision model, we cleverly divide the working state of the gas turbine into different gears to meet its requirements. To reduce the fitting difficulty and improve the result accuracy and convergence stability, a linear penalty term was added to the reward function. Considering the operating and environmental costs, in addition to the purchase and sale price of a large power grid, peak and valley electricity price and operation constraints, we further reduce the peak and valley difference and reduce the operating cost. Finally, the output of the battery and the output of the distributed power supply are obtained through simulation verification. Through the peak-shifting and valley-filling function of the battery, it can effectively deal with the fluctuation of peak and valley electricity prices, reducing the peak and valley difference of electric heating load, and providing strong support for sustainable energy management.

Design approach for carbon-neutral and resilient residential communities: Case study of Tabuk region

In this paper, a systematic analysis approach is outlined to design carbon neutral and resilient residential communities in Tabuk region, Saudi Arabia. The design used cost optimization approach to determine the required capacities for on-site power generation and energy storage using renewable energy resources to achieve desired carbon neutrality and energy resiliency thresholds. Moreover, different energy efficiency levels for the housing units are considered to design grid-connected residential communities based on prevalent grid electricity prices and carbon emissions. The analysis indicates that combinations of energy efficiency measures, PV systems, and storage batteries can be utilized to achieve both desired carbon neutrality and energy resiliency levels. The implementation of proven energy efficiency measures can reduce by over 45 % both the annual electricity demands, and the on-site PV capacities needed to reach carbon neutral designs for the residential communities. The cost-effectiveness of carbon-neutral and resilient designs depends significantly on installation costs of PV systems and wind turbines as well as electricity prices purchased and sold back to the grid. The levelized cost of energy for net-zero energy communities can be as low as 0.06 USD/kWh to reach carbon neutrality.

Optimal energy management and techno-economic assessment of hydrogen energy production system incorporated with photovoltaic and battery storage

Green hydrogen (H2) has garnered significant attention in recent years as a key component of a zero-emission future owing to its diverse range of applications. The energy management (EM) strategy of the green H2 production system (HPS) plays a crucial role in facilitating efficient and cost-effective green H2 production. This can be achieved by regulating the operating point of the electrolyzer (EL) to obtain economic and operational benefits. This study proposes an optimal EM scheme for a photovoltaic-based battery (BT) grid-connected green HPS. The HPS is intended to meet H2 and electricity demands of an industrial HPS. The objective of the EM scheme is to minimize the cost of green H2 production by minimizing the total system costs of the HPS, while ensuring that the system operates reliably. The objective is realized by incorporating the EM strategy model with Z-score statistical values of past grid electricity costs, which reflect seasonal electricity price trends. This enables green H2 production when the utility grid electricity prices are relatively low. The EM variables considered for the optimization include: EL status (on/off operation of the EL), electricity purchased from the grid, input power to the EL and output power from EL (H2 production rate), energy stored in the H2 storage tank and BT, discharging and charging power of the BT and H2 tank. Four test case systems (Case #1 - #4) were used to demonstrate the techno-economic benefits of the proposed EM scheme model for green HPS. Finally, a sensitivity analysis is performed on the green HPS to investigate the impact PV power and EL capacity on the cost of H2 and EL efficiency.

How to use BAPV to alleviate the urban heat island effect: An evolutionary game perspective.

In recent years, the phenomenon of the urban heat island caused by the rapid development of cities is very serious. To solve the problem of the urban heat island, this study proposed a PPP project consisting of the government (GOVT), photovoltaic investment company (PVIC), and residential customers (RS). Based on an evolutionary game model and combined with current policies and industry regulations in China, the evolution process and stable evolution strategies were studied. The result shows that more government subsidies, higher carbon trading prices, and feed-in tariffs will promote the development of the PPP project. For relatively suitable reference value ranges, the installation tilt angle of the BAPV system is 30°, the photovoltaic grid electricity price is 0.1096∼0.1296 $/kWh, the carbon trading is 8.92∼9.42 $/t.

Optimal design of PV-based grid-connected hydrogen production systems

A cost-optimal design of power-to-hydrogen (PtH) systems is crucial to produce hydrogen at the lowest specific cost. New challenges arise when it comes to ensuring a reliable and cost-effective hydrogen supply in the presence of variable renewable energy sources. In this context, the aim of this analysis is to investigate the optimal design of PV-based grid-connected hydrogen production systems under different scenarios. To this end, an optimisation framework based on the mixed integer linear programming (MILP) technique is developed. Results are presented by employing a set of techno-economic and environmental indicators to provide general guidance on how to optimally size PtH systems, going beyond the analysis of a specific case study. The analysis is applied to Italy and particular attention is paid to exploring the impact of the price of grid electricity.The results indicate that the price of grid electricity strongly affects the optimal design of PtH systems. Specifically, in scenarios with high electricity prices, it is economically convenient to significantly oversize the PV plant and the electrolyser. The optimal PV ratio, representing the ratio between the PV size and the electrolyser size, increases from 1.6 to 2.7 as the electricity price rises from 50 to 300 €/MWh. Additionally, when electricity prices exceed approximately 120 €/MWh, the optimal electrolyser size (in terms of hydrogen production under rated conditions) becomes almost three times larger than the average hydrogen demand. By comparing grid-connected and off-grid scenarios, the importance of the electrical grid is also highlighted: even when poorly used, it plays a crucial role in limiting the size of the hydrogen storage. The levelised cost of hydrogen for the optimal PtH configuration falls within the range of 3.5–7 €/kg (depending on the price of grid electricity) and increases to 8.2 €/kg when the system operates off-grid. Finally, the hydrogen carbon footprint, quantified as kgCO2,e/kgH2, is also explored. Considering the current price and carbon intensity of grid electricity, the cost-optimal PtH configuration already involves the production of renewable hydrogen (<3 kgCO2,e/kgH2).

Multi-level framework for optimal operation of gasifier system utilising crop residues from small and medium-scale farms

Lack of techno-economic framework for optimal gasification and the identification of critical parameters for optimal operations is one of the major challenges restricting the gasification of crop residues. This study aims to develop an optimal techno-economic framework for the gasification of crop residues from clustered small/medium-scale farms. The developed model was applied to a case study in Adiembra, a farming community for a 10-kW gasifier engine system. Eight scenarios of individual feedstock and their blends were considered. The results revealed specific fuel consumption ranging from 1.79 – 3.53 kg/kWh. The economic analysis showed marginal profitability except for rice husk and straw which are not profitable. At the current grid electricity price, the minimum level of subsidies required to ensure the financial viability of the feedstocks is within the range of 30 – 70 % of the investment cost based on the various feedstock scenarios considered. The study revealed individual feedstocks with the best technical and economic prospects for optimal gasification to be cocoa pod husk, maize stalk and husk, maize cobs, rice straw and rice husk in the order of best to worst. The use of feedstock blends generally improved the overall syngas characteristics and financial viability. A total number of farms ranging between 107 – 532 are required to be clustered within a radius of 0.74 – 2.12 km with a cluster radius greater than 3.91 km not being financially viable. The fraction of each feedstock type in the blends were optimised with corresponding increase in syngas generation within the range of 9 – 35 % and decrease in the required number of farms within the range of 30 – 57 %. The outcome of the study demonstrates that sustainable gasification of crop residues for minigrid electricity generation requires co-gasification of various residue types, valorisation of by-products and increase in the current feed-in-tariff rate in Ghana.

A stochastic vehicle schedule model for demand response and grid flexibility in a renewable-building-e-transportation-microgrid

Vehicle-to-building interaction as one of emerging and promising techniques shows powerful functions, to address intermittence of renewable energy, stochastic demand of buildings, power grid stability, and frequency regulation. The energy interaction magnitude is highly dependent on stochastic vehicle schedules, whereas a few studies focus on stochastic model development and guidelines on vehicle schedules. In this study, a series of stochastic models were developed to characterise the arrival time, departure time, and detention time-duration of vehicles in buildings. Considering the schedule difference between private cars and shuttle buses, both Chi-square distribution and Normal distribution were adopted, to quantify the distribution degree of vehicle numbers, together with parametrical analysis on standard deviation. Comparative analysis between different stochastic models can guide vehicle schedules and provide flexible vehicle-to-building interactions. Research results indicate that, normal distribution on arriving time and detention time-duration of vehicles shows the minimum annual import cost (ICannual) at 128.1 HK$/m2·a, while the normal distribution on arriving time and departure time with Chi-square distribution for detention time-duration shows the minimum equivalent CO2 emission (ECEannual) at 38.6 kg/m2·a. Furthermore, in respect to the distribution degree of vehicle numbers, the sharper curve for vehicle numbers in buildings (with lower standard deviation) will lead to a lower ECEannual and ICannual. Dynamic energy management strategy for the optimal stochastic vehicle schedule indicates that, marginal impacts on import cost saving can be provided to local power grid due to the limited difference between off-peak and peak grid electricity prices. This study provides new insight into the stochastic vehicle schedule model for an interactive vehicle-to-building energy interaction framework. Research results can provide frontier guidelines on stochastic vehicle schedules and flexible vehicle-to-building interactions for techno-economic-environmental performance improvement.

Cost-effective optimization for electric vehicle charging in a prosumer household

The operating expenditures of electric vehicles (EVs) consist mainly of the electricity used to charge the EV battery and of the replacement of wearing parts. This paper aims to optimize the EV battery charging as cost-effectively as possible for a household with a small-scale photovoltaic (PV) power plant. The optimization is carried out by problem-based optimization to closely match the average Finnish user behavior. For reference, an optimization was also performed disregarding the user behavior to make EV charging as cost-effective as possible. Cost minimization was done by using Nord Pool Spot market data to find the cheapest grid electricity prices, employing a perfect PV forecast, and using only surplus PV electricity for EV charging. Six years’ worth of data were analyzed, and it was found that the annual savings with average user behavior ranged between 25.4% and 51.9% when comparing optimized charging with uncontrolled charging. When disregarding the typical user behavior and having the EVs chargeable at all times, the annual savings ranged between 38.3% and 54.5%. The results show that the more the user uses the EV daily, the more cost-effective the optimization becomes compared with uncontrolled charging methods. The results also show that depending on daily fluctuations in the electricity prices, it is sometimes more beneficial to sell the produced PV power rather than use it to charge the EV and instead charge the EV with grid electricity at nighttime.

Navigating the Cost-Efficiency Frontier: Exploring the viability of Grid-Connected energy storage systems in meeting district load demand

In this investigation, we explored the cost-effectiveness and operational efficiency of grid-connected Energy Storage System (ESS) technologies—specifically, Proton Exchange Membrane-Reversible Fuel Cell (PEM-RFC) and Li-ion Battery (LIB)—with the goal of meeting load demand. Our conclusion reveals that integrating ESS isn't the optimal solution due to impractical implementation costs, making reliance solely on grid electricity a more pragmatic choice. Despite this, a secondary optimum value emerged, suggesting economic viability for ESS integration and introducing a dynamic trade-off between cost considerations and energy storage utilization. Furthermore, our study conducted a sensitivity analysis, anchored in the target costs set by the US Department of Energy, to identify conditions under which ESS systems could transition from impractical to feasible. The results outlined the potential to reduce the levelized cost of energy (LCOE) but also emphasized the prospect of increasing the storage factor by optimizing various system characteristics. Crucial factors like capital cost, efficiency, and lifetime were identified as pivotal in shaping the economic landscape of ESS integration. The study showcased tangible outcomes upon sensitivity analysis, including a 12% reduction in PEM-RFC LCOE and 58.5% reduction in LCOE for LIB, indicating improved cost efficiency and feasibility of ESS integration to the grid. To address a critical research gap, this study provides a novel comparison between PEM-RFC and LIB technologies as ESS, considering dynamic grid electricity prices. It sheds light on intricate dynamics that must be considered in ESS deployment decisions, emphasizing the delicate balance between cost-effectiveness and operational efficiency. Focused on the electricity load demand of commercial buildings in the Santa Barbara district, our techno-economic assessment contributes valuable insights for informed decision-making in ESS integration.

Profitability of Alternative Battery Operation Strategies in Photovoltaic Self-Consumption Systems under Current Regulatory Framework and Electricity Prices in Spain

The decreasing costs of solar photovoltaic (PV) technology have led to an exponential growth in the use of PV self-consumption systems. This development has encouraged the consideration of battery energy storage systems (BESS) as a potential means of achieving even more independence from the fluctuating grid electricity prices. As PV technology and energy storage costs continue to decline, both technologies will likely play an increasingly important role in the renewable energy sector. The profitability of batteries in PV self-consumption systems is largely influenced by the price of consumed electricity and the price at which surplus energy is remunerated. However, strategies in PV-BESS self-consumption systems typically do not take electricity prices into consideration as a variable for decision making. This study simulates and analyzes battery operation strategies that take into account electricity prices. The simulations are performed using real industrial consumption data and real electricity prices and tariffs, they cover the entire lifespan of the batteries, and include aging and degradation due to use and cycling. A techno-economic model is used to evaluate the advantages of incorporating these battery operational strategies into an actual PV-BESS system. The results demonstrate that the proposed strategies enhance the savings that batteries can provide.

Techno-Economic Analysis of Reversible and Paired Solid Oxide Cell Systems for Hydrogen Production

The United States (U.S.) Department of Energy (DOE) National Energy Technology Laboratory (NETL) has been pursuing the development of solid oxide cell (SOC) technology to enable future power generation systems with high efficiencies and low emissions. Government and industry net-zero goals are driving interest in approaches to decarbonize challenging sectors of the economy, such as industrial applications. Hydrogen (H2) – including that produced by SOC technology-based high-temperature electrolysis – is one promising route. At present, low prices for low-greenhouse gas intensity electrical power (i.e., renewable sources such as wind and solar) are available intermittently (at low-capacity factors), complicating the economics of clean hydrogen produced by electrolysis. Reversible solid-oxide cell (r-SOC) based power to gas systems have the potential to address this challenge by producing power when electricity prices are high and hydrogen when they are low. Plants with the ability to co-generate power from solid oxide fuel cells (SOFC) and hydrogen from solid oxide electrolysis cells (SOEC) are of particular interest for their dual revenue streams and the flexibility they can provide to the grid.This study investigates the cost and performance of two configurations of co-generating SOC plants. The first includes an r-SOC stack that alternates between power and hydrogen production. The second plant contains paired SOFC and SOEC stacks that are each dedicated to generating a single product when operated. For the purposes of this study, the plants are primarily intended to generate hydrogen, only switching to power production mode when market conditions are favorable. The study aims to elucidate expected tradeoffs between the higher capital cost of the paired SOFC/SOEC plant and the higher degradation rate of the r-SOC.The anticipated tradeoff of a reversible system is an increased degradation rate for lower capital costs. This is due to the paired systems being optimized for either fuel cell or electrolysis operation while the reversible system is not optimized for either. To quantify the impact of this, a base case where the SOFC, SOEC, and r-SOC all degrade performance-wise at the same rate (0.2% per 1000 h). The degradation rate of the r-SOC is then allowed to float to where the levelized cost of hydrogen (LCOH) of the two systems is at parity, which does not occur at the base case assumptions. Additional sensitivity analyses are performed on natural gas price and cost of imported power to determine which system is more favorable under different assumptions.In summary, the results show that the total plant cost of the paired SOFC/SOEC system is about 5% higher than the reversible system. The additional cost of having two separate SOC modules is largely diluted by the balance of plant costs. The LCOH of the r-SOC and paired SOFC/SOEC are ≈$3.60/kg and ≈$3.30/kg H2, respectively as shown in Figure 1. Despite the higher capital cost, the paired SOFC/SOEC has a more favorable LCOH. This is due to the SOFC’s ability to generate electricity cheaper than the assumed $60/MWh grid purchase price. Sensitivity analyses show how the assumed cost of grid power and natural gas price affects the disposition of hydrogen versus electron generation. As an example, the LCOH of the r-SOC becomes more favorable than the paired SOFC/SOEC at natural gas prices above $12/MMBtu and at electricity purchase prices below $27/MWh.In conclusion, this investigation shows that depending on the market situation and final application, reversible SOC units warrant consideration for inclusion in advanced integrated energy systems as grid electricity prices continue to decrease over time.For the purposes of this study, natural gas is used as the fuel for power generation operation, and the systems are equipped with high rate (≈98%) carbon capture. In future work, stored hydrogen may be examined as the fuel for power generation. Future analyses may also examine the use of industrial or other waste heat sources, as well as perform life cycle analyses for the cradle-to-gate greenhouse gas intensity for select system configurations and applications. Figure 1

Techno-economic assessment of a power-to-green methanol plant

Power-to-Liquid plants are a potential linchpin of future energy systems. Economic evaluations are crucial to assess their competitiveness with conventional fossil-based and other innovative processes. A techno-economic assessment of a previously presented plant concept producing green methanol was performed, evaluating different scenarios based on the net present value and annuity method. A hybrid plant was designed to realize an availability comparable to industrial plants. Green methanol prices of 717–1107 €2022/t are required to realize amortization periods between 5 and 25 years. Net production costs of 785 €2022/t were obtained for the base scenario, a surcharge of 44% compared to German methanol prices in 2022. CO2 prices of 220–310 €2022/t are necessary to become cost-competitive with fossil-based methanol. A sensitivity analysis underscored the grid electricity price’s negligible influence on the plant’s economic performance due to the implemented hybrid power plant. Green methanol has the potential to decrease the chemical industry’s carbon footprint while simultaneously substituting fossil imports with local CO2 sources. The presented study provides a concept tackling Power-to-Liquid plants’ major drawbacks by concurrently maximizing annual operating hours and economic performance due to the utilization of renewable electricity. Consequently, stable long-term scenarios for interested investors were established.

Techno-economic analysis of large-scale green hydrogen production and storage

Producing clean energy and minimising energy waste are essential to achieve the United Nations sustainable development goals such as Sustainable Development Goal 7 and 13. This research analyses the techno-economic potential of waste heat recovery from multi-MW scale green hydrogen production. A 10 MW proton exchange membrane electrolysis process is modelled with a heat recovery system coupled with an organic Rankine cycle (ORC) to drive the mechanical compression of hydrogen. The technical results demonstrate that when implementing waste heat recovery coupled with an ORC, the first-law efficiency of electrolyser increases from 71.4% to 98%. The ORC can generate sufficient power to drive the hydrogen's compression from the outlet pressure at the electrolyser 30 bar, up to 200 bar. An economic analysis is conducted to calculate the levelised cost of hydrogen (LCOH) of system and assess the feasibility of implementing waste heat recovery coupled with ORC. The results reveal that electricity prices dominate the LCOH. When electricity prices are low (e.g., dedicated offshore wind electricity), the LCOH is higher when implementing heat recovery. The additional capital expenditure and operating expenditure associated with the ORC increases the LCOH and these additional costs outweigh the savings generated by not purchasing electricity for compression. On the other hand, heat recovery and ORC become attractive and feasible when grid electricity prices are higher.

Electric vehicle charging and discharging scheduling strategy based on dynamic electricity price

The rapid growth in the number of electric vehicles (EVs) has significantly increased the demand for electricity for residents. In addition, because the charging time of EVs highly coincides with the peak period of user electricity consumption, the disorderly charging of EVs will lead to the overload of the power grid transformer. Traditional control methods lack certain robustness and do not fully consider the uncertainty of EVs. As a result, the V2G participation rate of electric vehicles cannot be determined, and the control reliability is low. To solve the above problems, this paper designs a reinforcement learning framework of Long Short-Term Memory network and Improved Linear programming algorithm (LSTM-ILP) to control the V2G of EVs.This paper comprehensively considers the overall electric vehicle charging demand, discharge potential, large grid electricity price, aggregator, and users’ interests demands. Firstly, aiming to minimize the charging and discharging fee of EVs and the load peak-to-valley difference of the power grid, a dynamic electricity price based on Long Short-Term Memory neural network (LSTM) is established. Then, the improved linear programming algorithm (ILP) is used to solve the charging and discharging optimization problem of EV, and the results are fed back to the input of the next iterative update of the LSTM, and finally, the optimal electricity price and EV charging and discharging schedule are achieved. The simulation results show that the LSTM-ILP framework can not only reduce the charging fee of electric vehicles, but also achieve the Peak and valley trimming of the grid load. Charging costs for EV users were reduced by 42.1% compared with unordered charging, and by 22% percent compared with orderly charging.

The realistic problem of benefits maximization—Take A Power Generation Co., Ltd., the fourth largest power group in China, as an example

Under the influence of power market reform, spot trading and coal price rise, the development of power enterprises is facing more and more uncertainties. Power enterprises need to reduce power generation costs as much as possible on the basis of ensuring power supply. For this reason, this paper discusses the continuous rise of coal prices faced by power enterprises, the small fluctuation and increase of on grid electricity prices, and the implementation of power market and spot transactions. This paper collects and summarizes data, and then analyzes costs and benefits, business conditions and national policies, and puts forward solutions. It concludes that power enterprises should start marketing on the basis of providing social security, the coal purchase and storage strategy and the direction of energy transformation will be changed to minimize its costs and maximize its benefits. This paper has reference value for the operation and development of major power enterprises.

Dispatch optimization of electric thermal energy storage within System Advisor Model

A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.

Commercial building integrated energy system: sizing and energy-economic assessment

Abstract Integrated energy systems are one of the potential options for buildings that can reduce emission. In this research study, the energetic and economic performance of a micro-gas turbine combined heating and cooling plant coupled with a solar PV is analyzed for an office building in Iran. For each analysis, two different scenarios have been performed. System sizing parameters defined in a way that renewable to fossil fuel share is correlated to plant performance and economy. To model the studied system, a time-dependent method is used, which is the inherent characteristic of renewable energies. The renewable energies used here are solar heaters and solar panels. Contours of Net Present Value (NPV) are evaluated as a function of solar heating share and different economic parameters. In addition, optimal system sizing for a typical building is obtained and the results are provided. Effect of various major parameters shows that under the current condition and despite the supportive incentive for renewable energies, strategies and plans even without solar energy are not economically viable due to the high discount rates. In addition, results provide that, in reasonable and normal discount rate, fuel and grid electricity prices, governmental subsidization for conventional combined heat, and power (CHP) and combined cooling, heat, and power(CCHP) is not necessary, and only in this condition solar electricity selling price (i.e. governmental support program) is effective to increase renewable penetration. The results show that if the interest rate is less than 5%, the NPV becomes positive. Also, when the electricity price reaches $0.07/kWh or higher, the NPV becomes positive.