Grid Electricity Price Research Articles

Optimal operation strategies of multi-energy systems integrated with liquid air energy storage using information gap decision theory

In this paper, a framework of multi-energy system (MES) integrating with a liquid air energy storage (LAES) system was proposed. LAES, where liquid air works as an energy storage media, is a powerful and eco-friendly technology for storing renewable energy resources and reducing grid curtailment. Considering the characteristics of LAES (i.e. cold and heat circulation), the incorporation of LAES system into the Combined Cooling, Heating and Power system can achieve integrated use of energy and effectively save energy. Moreover, the prices of electricity will affect the overall cost of the MES. In other words, the decision-makers of the MES need to consider the uncertainty of electricity prices when making power dispatching decisions. To model the uncertainty of electricity prices, the information gap decision theory method was used to study power dispatching strategies of the MES. Three different strategies were proposed, including risk-neutral, risk-averse and risk-taker. In addition, demand response algorithms were used to study load transfer strategies. The results show that the demand responses of the three strategies are effective in terms of load transfer and cost saving. The total operation cost in the risk-neutral strategy with demand response can be 6.82% less than that without demand response; In the risk-taker strategy with demand response, the allowable grid electricity price is reduced by 25.24% when the opportunity cost drops by $8,000, and 23.32% without demand response. With additional robustness cost, the acceptable price change ratio using demand response is 21.91% in the risk-averse strategy, and 20.04% without demand response.

Quality-Aware Video Streaming for Green Cellular Networks With Hybrid Energy Sources

Mobile video traffic has experienced explosive growth in recent years due to the rapid development of mobile intelligent terminals and cellular communication technologies. The rapid growth of mobile video traffic has brought significant energy expenditure for mobile network operators. To reduce the energy expenditure, one promising solution is to exploit renewable energy harvested from surrounding environments for cellular traffic delivery. In this article, we investigate mobile video streaming in green cellular networks with hybrid energy sources, i.e., grid energy and ambient energy, to optimize both video quality and energy expenditure. Specifically, we formulate a stochastic optimization problem to maximize the long-term time-averaged network service utility, which is the difference of video quality and energy expenditure. The problem formulation takes the following factors into account: time-varying grid electricity price, energy harvesting process, and different time scales of rate adaptation (RA), resource management, and electricity price fluctuation. We exploit Lyapunov optimization framework to decompose the problem into three subproblems: 1) RA subproblem; 2) battery energy management subproblem; and 3) joint power control and subchannel assignment subproblem. We propose an efficient online green video streaming algorithm to solve these subproblems. We analyze the stability of the proposed algorithm with respect to lengths of energy queue and user request queues. Extensive simulations are conducted and the results validate the efficiency of the proposed algorithm.

Off-grid PV/URFC power plant fueled with biogas from food waste: An energetic and economic analysis

This paper presents the design, energy management and economic analysis of an off-grid power plant for a strip mall. The main goal is to achieve a 100% renewable operation, by using a photovoltaic panel array coupled to a battery and a unitized regenerative solid oxide fuel cell fed both with self-generated hydrogen and biogas produced from food waste in an on-site anaerobic digester. The power plant has been modeled by means of a self-made Matlab/Simulink® simulator and the plant operation has been simulated against different radiation and load demand scenarios. Results are compared both in terms of energetic and economic performance. In particular, it is observed that the annualized capital costs for unitized regenerative solid oxide fuel cell cover from 50% up to 58% of the total investment cost and from 71% up to 75% of the total replacement costs. Depending on the scenario and the specific tipping fee, tipping costs vary from a minimum of 9,793 $/y to a maximum 47,335 $/y. For all the analyzed scenarios, the cost of energy is higher than the grid electricity price, but the 100% renewable operation of the plant and the on-site waste conversion to biogas make this solution worth of investigation.

Microgrid energy management strategies assessment through coupled thermal-electric considerations

Battery Energy Storage Systems (BESSs) are considered as one of the most promising technological options towards high self-sufficiency of building facilities and higher independency from the main grid through autonomous operation. At the same time, BESS are an essential component for a cost-optimized operation of grid-connected microgrids. In addition, the ever-increasing trend to replace conventional heating systems with electrically driven heat pumps, reveals a continuously emerging trend for electrification supported by electrical storage systems. In this study, a grid-connected microgrid of a sport centre facility located in Barcelona (Spain), which includes i) a solar PV (Photovoltaic) installation, ii) a BESS and iii) a heat pump (HP), is investigated through dynamic simulations, considering both thermal and electrical loads. Targeting the best compromise between self-sufficiency and reduction in the cost of electricity, two different energy management strategies are examined for the supervision and control of the grid-connected microgrid operation, taking into account the impact of the already operating HVAC (Heating Ventilation and Air Conditioning) systems along with the HP operation. In particular, the investigated energy management strategies are considered to be two of the most prominent ones and involve 1) peak shaving of the microgrid consumption with off-peak grid power and 2) pricing-based operation of the BESS (arbitrage), according to the main grid electricity price. Under this scope, an integrated thermal and electrical dynamic model of the sport centre energy system is developed in APROS software. The results are benchmarked against the corresponding ones from the standard model of TRNSYS. The impact of each management strategy is assessed in terms of the resulting power flows and the associated cost of electricity, for each operational mode. Based on the simulation results, the most advantageous energy management algorithm is determined for each case study, revealing that the integrated thermal-electrical dynamic modelling can be a useful tool for designing adaptive energy management algorithms.

Using advanced battery storage to cut energy costs

Battery energy storage systems at Shell manufacturing plants in Canada are activated when the price of grid electricity goes up, helping the plants to reduce their overall energy costs.

Design of hybrid power-to-power systems for continuous clean PV-based energy supply

The increasing penetration of intermittent renewable sources, fostering power sector decarbonization, calls for the adoption of energy storage systems as an essential mean to improve local electricity exploitation, reducing the impact of distributed power generation on the electric grid. This work compares the use of hydrogen-based Power-to-Power systems, battery systems and hybrid hydrogen-battery systems to supply a constant 1 MWel load with electricity locally generated by a photovoltaic plant. A techno-economic optimization model is set up that optimizes the size and annual operation of the system components (photovoltaic field, electrolyzer, hydrogen storage tanks, fuel cell and batteries) with the objective of minimizing the annual average cost of electricity, while guaranteeing an imposed share of local renewable self-generation. Results show that, with the present values of investment costs and grid electricity prices, the installation of an energy storage system is not economically attractive by itself, whereas the installation of PV panels is beneficial in terms of costs, so that the baseline optimal solution consists of a 4.2 MWp solar field capable to self-generate 33% of the load annually. For imposed shares of self-generation above 40%, decoupling generation and consumption becomes necessary. The use of batteries is slightly less expensive than the use of hydrogen storage systems up to a 92% self-generation rate. Above this threshold, seasonal storage becomes predominant and hybrid storage becomes cheaper than batteries. The sale of excess electricity is always important to support the plant economics, and a sale price reduction sensibly impacts the results. Hydrogen storage becomes more competitive when the need for medium and long terms energy shift increases, e.g. in case of having a cap on the available PV capacity.

Feasibility Assessment of a Grid-Connected Carbon-Neutral Community in Midland, Texas

AbstractMidland, Texas is one of the fastest-growing urban population centers in the country and has one of the lowest costs of electricity. This study aims to assess the potential of a grid-connected carbon-neutral community in an oil-rich city using energy efficiency measures and hybrid distributed generation (DG) systems. The community consists mostly of residential buildings including detached homes and apartment buildings. Moreover, a cost-optimization analysis of various DG technologies is carried out to meet both electrical and thermal loads of the community in Midland. The energy efficiency measures are selected for two main objectives: (i) reduce the total energy needs and (ii) electrify most of the buildings within the community. Improvement of heating, ventilating, and air conditioning systems and their controls are the main energy efficiency measures considered for all the buildings part of the community. DG systems are constrained by the renewable energy resources identified to be prevalent within the site of the community. It is found that photovoltaic (PV) systems are the most cost-effective, while wind and combined heat and power (CHP) would not be competitive compared to the current grid energy prices. Specifically, the optimization results indicate that PV, when implemented on a large scale, can provide adequate power to meet the energy needs of the community while also meeting carbon neutrality. A PV system size of 3400 kW is found to be required for the grid-connected community to be carbon neutral. While under this scenario a 100% reduction in carbon emissions is technically feasible, the cost of energy is estimated to be $0.194/kWh, almost double the current grid electricity price. However, if the capital cost of PV is decreased by 70% from its current level, the cost of energy due to the DG addition can be reduced significantly. In particular, a 1050-kW PV system was found to reduce the cost of energy below the grid electricity price of $0.10/kWh and achieves 31% reduction in carbon emissions for the community. Moreover, the 70% reduction in PV capital costs allows the carbon-neutral design for the community to be a cost-competitive solution with the grid.

A Comparative Study of Recent Optimization Methods for Optimal Sizing of a Green Hybrid Traction Power Supply Substation

Although there is a premise that electric trains are zero-emission, their source of energy (fossil fuel power plants) pollutes the air in a place far from the consuming area (traction power supply substation). On the other hand, the price of generating electricity from fossil fuel resources has risen in the aftermath of their ever-decreasing sources. These two economic-environmental factors have caused Hybrid Renewable Energy Sources (HRESs) to be introduced as an alternative to fossil fuel ones. This paper proposes the concept of Green Hybrid Traction Power Supply Substation (GHTPS), that is, using renewable energy resources to meet a traction substation. To find the best size of HRES components having a minimum Lifecycle cost; an optimization method is essential. For this reason, a comparative study on the application of recent optimization methods is employed to find the optimum size of the proposed grid-connected PV/wind turbine traction substation. The optimization methods are: the Atom search optimization (ASO), Harris Hawks Optimization (HHO), Coyote Optimization Algorithm (COA), Multi-population Ensemble Differential Evolution (MPEDE), Bird Swarm Algorithm (BSA), Ant Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and HOMER software. Finally, a sensitivity analysis shows that increasing in the grid electricity price and decreasing the wind turbines investment cost could make renewable energies more economically competitive in the future. Besides Net Present Cost (NPC), Cost of Energy (COE), Payback Time (PT), and various emissions are studied, all of which verify the efficiency of the proposed system.

Economic evaluation of a hybrid heating system in different climate zones based on model predictive control

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.

Singular value decomposition‐based load indexes for load profiles clustering

Choosing suitable load indexes of load profiles is of vital importance for load profiles clustering, which has wide applications in load forecasting, power grid planning and electricity price designing. To obtain a set of load indexes with rigorous mathematical theory and clear physical meaning, this study proposed a singular value decomposition (SVD) based method to extract indexes. First, empirical rank-l approximation derived from SVD is proposed to extract load indexes. The relationship between singular values and relative approximation error guarantees the indexes retain major characteristics of load profiles, while the invariance of right singular vectors over seasons and sample sizes endows the indexes with good generalisation ability. Then the right singular vectors are discretised to determine definition of indexes and reveal physical meanings of the indexes. Finally, the new set of indexes extracted by the proposed method are applied in indirectly clustering in the case study, which verify the validity of the indexes, the performance of the clustering method and the advantages of the new indexes over the existing load shape indexes.

Sustainability evaluation and sensitivity analysis of district heating systems coupled to geothermal and solar resources

District heating systems assisted by renewable energy harness local energy sources, save fossil fuels, and reduce greenhouse gas emissions. Here, geothermal and solar resources are integrated into district heating through a geothermal heat pump and an absorption heat pump by employing vapor-compressor and absorption cycles, respectively. A quantitative sustainability assessment model was constructed for the resulting hybrid district heating systems. Indicators on energy, environment, economic, and societal aspects were used to evaluate the composite sustainability index of hybrid systems with information entropy method. The results demonstrate that a district heating system coupled to compound parabolic concentrator-photovoltaic/thermal solar collector system with 100% photovoltaic coverage ratio is the ideal system for the case study when the annual cost saving ratio is given the highest impact in the composite sustainability index. A detailed analysis on the annual cost saving ratio shows that a higher ambient temperature, solar beam irradiance and grid electricity price would have a positive impact on the annual cost saving ratio. It can be concluded that the quantitative sustainability evaluation approach proposed helps to select the ideal hybrid system.

Modeling the Total Cost of Ownership of an Electric Car Using a Residential Photovoltaic Generator and a Battery Storage Unit—An Italian Case Study

As electric vehicles gain acceptance, an increasing number of households consider the possibility of buying the bundle including an electric car, a photovoltaic system, and a battery storage unit. Apart from the attractive environmental benefits, a relevant uncertainty concerns the economic convenience of such a choice. Since many variables play a role, we set up a total cost of ownership model to evaluate whether, and under which conditions, the bundle is cost-competitive relative to buying an electric car only (and charging it from the electrical grid) or a conventional combustion engine car. By combining, for the first time, such an economic model with an energy model and a driving profile model, we find that the degree of electricity self-production used to charge the electric car might be very high, varying from 90% to 62%, depending on the annual distance traveled. The cost of such electricity varies widely and can be lower than the grid electricity price when fiscal incentives are available and for long annual distances traveled. A smart charging practice based on both economic factors and weather forecast can greatly enhance self-sufficiency, i.e., independence from the electrical grid. We estimate that, given the current Italian financial incentives, 10,000 km/year are needed to make the electric car cost-competitive with respect to an equivalent petrol-fueled one. Such threshold increases to more than 25,000 km/year if financial incentives are removed.

Realistic and Intelligent Management of Connected Storage Devices in Future Smart Homes Considering Energy Price Tag

Coordinated energy management plays a main role in increasing the performance and economic benefits of future smart homes. This article focuses on the energy management of a smart home equipped with a plug-in electric vehicle (PEV), household battery storage, and photovoltaics (PV), and it proposes an energy price tag (EPT) for all energy storage devices connected to the smart home system. A hybrid approach that consists of optimization and a rule-based prioritization is presented. The proposed algorithm establishes a priority order between the PEV, the household battery, and the imported power from the grid based on the EPT of energy sources. The proposed energy management algorithm seeks for the minimum overall energy cost for the smart home and PEV owner while satisfying household power demand and charging requirements of the storage devices. The performance of the energy management algorithm is examined on a typical smart home over a 24-h period based on time-varying grid electricity price.

Solutions for improving the energy efficiency in wastewater treatment plants based on solid oxide fuel cell technology

Polygeneration configurations for small power generation systems offer significant potential for energy saving and reducing carbon emissions in wastewater treatment facilities. In this work, a biogas-fed solid oxide fuel cell system operating in a wastewater treatment plant (located in Turin, Italy) is analyzed in terms of its potential improvements through novel polygeneration systems. In its present combined heat and power configuration, along with electrical power, thermal energy from the exhaust gas is recovered to provide required heat to the plant’s anaerobic digester. The analysis is focusing on different energy efficiency solutions for this type of plant by using solar thermal collectors, microturbines, a trilateral Rankine cycle, and an absorption chiller. Results reveal that, despite of higher efficiency for the trigeneration case using both trilateral Rankine cycle and absorption chiller (up to 88.4%), the solar integrated system results in the lowest natural gas consumption, which is 38.5% lower than the baseline scenario. This same scenario is also the worst in economic terms due to the high capital costs of solar collectors. In a short-term cost trajectory of the solid oxide fuel cell technology, the most economically favorable scenario is the microturbine integrated case in which the calculated levelized cost of electricity is 0.11 €/kWh, lower than grid electricity price, and with payback time of 6.5 years. Long-term cost trajectory is indeed generating effective investments for all of the four scenarios with payback time between 3 and 5 years in all cases. The analysis has been developed to the entire European Union area: the most suitable market conditions are found in Germany, Denmark, Slovakia, and Italy.

Development of solar home system with dual energy storage

Distributed energy generation with energy storage is quite important for high penetration of solar PV energy. A solar home system which generates solar power for self-consumption was studied. The solar home system utilizes a switching-type solar PV (HyPV) which operates in either solar or grid mode automatically without feeding solar power into grid. The solar home system also uses dual energy storage consisting of a battery and an electric water heater which stores PV energy as hot water (thermal storage) when the battery is full. Since thermal storage is much cheaper than battery, the dual energy storage would reduce the total cost of solar home system dramatically. Four HyPV solar home systems were built to run long-term field test. The results show that the specific PV energy generation approximates that of feed-in-tariff PV system. A solar PV energy production cost analysis shows that the HyPV solar home system with dual storage is economic if it was used to replace partial energy demand which is paid at higher grid electricity price. This verifies the possibility of grid parity for a solar PV system with energy storage.

Assessment of urban-scale potential for solar PV generation and consumption

The rise of grid electricity price and a growing awareness of climate change is resulting in an increasing number of photovoltaic facilities installed in buildings. Electricity market regulation and climatic conditions, in particular solar radiation, are the main factors that determine the economic viability of a photovoltaic facility. This paper describes a method for evaluating the potential for photovoltaic (PV) production and self-consumption for the building stock of a particular city. A GIS 3D city map is used to calculate solar irradiation. Building-level electricity use is calculated based on building type, geometry and other characteristic inferred from building age, taking the cadastre GIS as main input. The methodology identifies the realistic potential for rooftop photovoltaic installations, as well as the optimum size to be installed from an economic perspective. To represent different regulations that can affect economic viability of PV installations, calculations should adapt for the specific installation conditions and regulatory situation, as for example self-consumption and net metering. The proposed methodology is applied to a case study in Irun (Spain), where results for potential of PV generation and self-consumption for the building stock are presented. The results offer public administration a realistic view of economically viable PV potential for the city and allow to analyse different mechanisms to promote their installations. It also serves for individual electricity consumers to evaluate and optimize new photovoltaic energy facilities. Finally, it serves policy makers to estimate the repercussion of electricity market regulations on the economic viability of PV systems.

Multi-objective optimal dispatching of microgrid based on improved genetic algorithm

Aiming at the optimization problem of micro-grid energy dispatch, the environmental benefit, system operation and maintenance cost, start-stop cost and main grid electricity price are proposed as the objective function, and the influence of the actual operation constraints of micro-grid system and the distributed non-scheduled new energy are taken into account at the same time. The multi-objective optimal dispatching model of microgrid is constructed, and the model is solved based on improved genetic algorithm. The numerical simulation results show that compared with the traditional single-target scheduling method of microgrid, the optimal scheduling method of this paper is beneficial to the micro-grid in the process of scheduling decision-making to achieve the overall coordination of economic, environmental and social benefits of microgrid.

Study on the problem of wind power curtailment in Beijing-Tianjin-Hebei based on risk-return

ABSTRACT The problem of wind curtailment is serious in China. It not only restricts the development of renewable energy, but also brings economic losses to the enterprises and the society. Unfortunately, the problem of wind power curtailment is difficult to eliminate under existing facilities and technical conditions. Therefore, it is a practicable way to control the wind power curtailment rate as far as possible on the premise of guaranteeing the profit of the power generation enterprise and meeting the demand for electricity users. In the context of the coordinated development of the Beijing-Tianjin-Hebei region, this article presents a deep analysis of other energy generation, power grid planning, social benefits, electricity prices, and other factors associated with the wind power generation, then we constructed a complete “cost of wind curtailment” model. Finally, an uncertainty analysis is added to show the impact of some uncertainties on the model, which makes the model more complete, and after calculating the cost and benefit of “reducing the rate of wind curtailment,” some pertinent suggestions are given, which will provide policy basis for the development of renewable energy.

Optimal control strategies for a hybrid renewable energy system: an ALANN/RNN technique

In this paper, optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the parallel execution of both the ant lion optimization with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. Here, the HRES is made out of photovoltaic, wind turbine, fuel cell, and battery which is associated with the DC link and can adjust the real and reactive power. The proposed ALANN/RNN technique predicts the required control gain parameters of the HRES to maintain the power flow based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy accessibility and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. In the HRES unit, the power flow management of grid is accomplished while controlling the PI controller for producing the optimal control pulses of the DC/DC converter. The proposed method is actualized in MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques such as SPC, GA, PSO and BAT technique. The comparison results demonstrate that the occurrence of proposed approach confirms its ability for controlling the power flow in the HRES system. An optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the joined execution of both the ant lion optimization (ALO) with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. ALO is utilized to minimizing the power variations of the HRES units. ANN is used for real power prediction, and RNN is utilized for reactive power prediction.

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.