Grid Tariff Research Articles

On the characterization and evaluation of residential on-site E-car-sharing

E-car-sharing concepts can play a key role in future urban areas as a new and sustainable transport system. When it comes to residential buildings, the added values of sharing systems are reduced parking space and increased use of solar photovoltaics generation on-site. This work proposes a simple and complete method to define residential building tenants’ optimal investment and operation, switching from car ownership to an e-car-sharing system. A mixed-integer linear optimization framework describes technologies such as battery storages, solar photovoltaics, electric vehicles, and charging stations. Furthermore, different scenarios are defined and simulated in order to investigate and evaluate the economic potential of residential on-site e-car-sharing. It is shown that with the actual grid tariff design in Austria, the integration of bidirectional charging stations is not cost-effective if considering only the Day-Ahead spot market. The results show that integrating an e-car-sharing system in a residential building allows for fewer cars and charging stations with higher nominal power, reducing the annual total costs of the residential building tenants by up to 29%. On the other hand, the results indicate that e-car-sharing systems can also lead to lower optimal installed solar photovoltaic power.

Long-term Value of Flexibility from Flexible Assets in Building Operation

In this work, we investigate how flexible assets within a residential building influence the long-term impact of operation. We use a measured-peak grid tariff (MPGT) that puts a cost on the highest single-hour peak import over the month. We apply a mathematical model of a Home Energy Management System (HEMS) together with Stochastic Dynamic Programming (SDP), which calculates the long-term impact of operating as a non-linear expected future cost curve (EFCC) from the end of the scheduling period to the start. The proposed model is applied to a case study for a Norwegian building with smart control of a battery energy storage system (BESS), Electric vehicle (EV) charging and space heating (SH). Each of the flexible assets are investigated individually with MPGT and for an energy-based grid tariff. The results showed that EV charging has the highest peak-power impact in the system, decreasing the total electricity cost by 14.6% with MPGT when controllable compared to a reference case with passive charging. It is further shown how the EFCC helps achieve optimal timing and level of the peak demand, where it co-optimizes both real-time pricing and the MPGT.

Integrating Thermal-Electric Flexibility in Smart Buildings using Grey-Box modelling in a MILP tool

In a smart grid setting, building managers are encouraged to adapt their energy operations to real-time market and weather conditions. However, most literature assume stationary temperature set points for heating and cooling. In this work, we propose a grey-box model to investigate how the energy flexibility of the thermal mass of the building may impact its energy flexibility potential as well as the investment decisions of the energy system within a building, by using an already developed investment decision tool, BUILDing’s OPTimal operation and energy design model (BUILDopt) (Lindberg et al. (2016)). As BUILDopt is a Mixed Integer Programming (MIP/MILP) tool, the flexibility models must be linear as well. We evaluate the energy flexibility potential, here called comfort flexibility, for use cases reflecting different heating systems (electric panel ovens vs. ground source heat pump) and operation (flexible vs. non-flexible). The case study of an Office building is performed, which considers electric specific demand, domestic hot water demand and space heating demand. Real historical data for weather and energy prices from Oslo are used, including grid tariffs related energy and monthly peak power. Most of the savings are obtained through peak load reduction, which can reach up to 13-16%. These and the savings from shifting demand away from peak prices lead to total savings of around 2%. Yet, these actions do not require additional investment in heat supply or storage components, nor in building renovations: only system measurement and control components are needed.

Impact of shared battery energy storage system on total system costs and power peak reduction in commercial buildings

The power system is experiencing an increasing share of renewable and intermittent energy production and increasing electrification. However, these changes are creating high power peaks, are straining the grid and call for expensive investments in expansions and improvements. This paper examines how the operational strategy of shared battery energy storage systems (s-BESS) can address these issues for commercial buildings with relatively high power peaks. Due to the uncertainty in long-term costs when subject to a measured peak (MP) grid tariff, the scheduling of the battery is optimised with a receding horizon control algorithm. The optimisation model is used on a Norwegian real-life case study to find the best possible configuration with an already existing battery. Although current Norwegian regulations challenge the possibility for shared metering and billing for a s-BESS configuration, the results show that the total system cost was reduced by 19.2% compared to no battery. The community peak was reduced by 17.8% compared to no battery and 6.22-17.5% compared to individual storage, which indicates that s-BESS is of value for the DSO as well.

An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming

This paper proposes an energy management system (EMS) for battery storage systems in grid-connected microgrids. The battery charging/discharging power is determined such that the overall energy consumption cost is minimized, considering the variation in grid tariff, renewable power generation and load demand. The system is modeled as an economic load dispatch optimization problem over a 24 h horizon and solved using mixed integer linear programming (MILP). This formulation, therefore, requires knowledge of the expected renewable energy power production and load demand over the next 24 h. To achieve this, a long short-term memory (LSTM) network is proposed. The receding horizon (RH) strategy is suggested to reduce the impact of prediction error and enable real-time implementation of the EMS that benefits from using actual generation and demand data on the day. At each hour, the LSTM predicts generation and load data for the next 24 h, the dispatch problem is then solved and the battery charging or discharging command for only the first hour is applied in real-time. Real data are then used to update the LSTM input, and the process is repeated. Simulation results show that the proposed real-time strategy outperforms the offline optimization strategy, reducing the operating cost by 3.3%.

Optimal sizing and energy scheduling of grid-supplemented solar PV systems with battery storage: Sensitivity of reliability and financial constraints

Establishing reliable, clean, and inexpensive solar PV systems is a complex interplay between the level of reliability (LPSP), financial constraints, and CO2 emissions. This paper investigates the impact of these factors on stand-alone (SA) and grid-supplemented (GS) solar PV systems over multiple seasons. The research uses established hardware models, detailed power management strategies as well as realistic Australian grid tariffs and Genetic Algorithms to find the minimum Cost of Energy (COE) subject to LPSP and financial constraints. The developed power management strategies are also tested experimentally on a real solar PV system. The results indicate that the grid-supplemented system yields 30% lower COE compared to the stand-alone at baseline (LPSP<0.01) but achieves this at the expense of 17% higher life cycle emissions (LCE, kgCO2-eq/kWh). The results also revealed that in the grid-supplemented systems, only optimum renewable to grid penetration ratios can yield minimum COE which were found to be 95% and 5% respectively in this case study. A typical increase in the COE with tighter LPSP is found for the stand-alone systems. Whilst in the grid-supplemented systems, higher system's reliability can be achieved at almost the same COE but with increased emissions. In terms of tariff structures, the time of use tariff structure offers a marginally lower COE (0.30$/kWh) compared to the anytime flat tariff (COE = 0.32$/kWh), but with the latter outperforming in terms of LCE. The analyses presented help identifying the parameters to be considered in establishing more cost-effective solar PV systems.

Feasibility and techno-economic analysis of stand-alone and grid-connected PV/Wind/Diesel/Batt hybrid energy system: A case study

In this study, the economic and environmental benefits of stand-alone and grid integration are thoroughly analyzed with different system configurations of a PV/Wind/Diesel/Battery based hybrid energy system (HES) for five different climatic regions using hybrid optimization model for electric renewables (HOMER). A detailed techno-economic study of optimized hybrid systems is further examined by integrating the grid-connected option. The environmental benefits of HESs are discussed. The sensitivity of various sell-back price to the national grid is also investigated. Additionally, the barriers and opportunities of installing such projects in the off-grid regions are discussed. Results indicate that the cost of energy (COE, $/kWh) and the net present cost (NPC, $) of the stand-alone hybrid PV/Diesel/Battery for the Rajshahi region are slightly lower compared to other areas, considering the cost and environmental emissions. The same system in Chattogram shows great potential both financially and environmentally, over the other climatic zones. The grid-connected HES with the sell-back option offers significant cost-benefits (0.07$/kWh), even over the grid tariff (0.10$/kWh). Similar revenues can be attained with the grid-connected PV/Battery-based system as substantial amount of excess energy could be supplied to the grid facilities. In the grid integrated HES, around 45,582 kg-CO2/yr could be saved compared to grid only system, whereas this amount is 32,905 kg-CO2/yr over the stand-alone hybrid PV/Diesel/Battery one.

Optimized Integration of Hybrid Renewable Sources with Long‐Life Battery Energy Storage in Microgrids for Peak Power Shaving and Demand Side Management under Different Tariff Scenario

Herein, a novel cost‐effective demand side management and peak power shaving are demonstrated by optimized scheduling of renewable energy source integrated grid‐connected hybrid microgrid and vanadium redox flow battery (VRFB) storage. To promote the waste to energy for the rural and urban communities, the biogas energy source is integrated along with the combined solar photovoltaic (PV) and wind energy sources. As a long life and scalable battery storage solution, VRFB storage is adopted for peak shaving and microgrid performance reliability. Power generation from the renewable sources, VRFB charge–discharge, and grid power usage are scheduled considering two practical electricity tariff profiles, thus making the overall microgrid system operation cost‐effective and efficient. The optimized cost of energy management is determined considering the operation and maintenance cost of energy source and battery storage, grid tariff profile, and power import and export. The performance of the overall control scheme is experimentally validated by a grid‐connected hybrid microgrid consisting of 10 kWp solar PV, 1 kW wind turbine, 15 kVA biogas engine generator, and 1 kW 6 h VRFB storage. The proposed energy management scheme is scalable and a generalized one that claims to be suitable for large‐scale renewable energy integrated power systems as well.

Impacts of electricity pricing on techno-economic performance of photovoltaic-battery centered microgrid

ABSTRACT The energy management technique in a microgrid, plays very crucial role for making it more economic viable during the electricity energy pricing dynamics. It is vital to investigate the impact of electricity energy pricing dynamics on operation and techno-economic performance of a micro-grid for maximizing the local energy participation with grid constraints. This paper has considered, a common PV-battery-based microgrid from Norway for assessing technical and economic performance with electricity energy pricing dynamics. In this work, energy management strategy has presented, for minimization of annual energy generation cost with maximization of battery energy throughput with grid constraints as network demand limits. It has been observed that grid buying price has more impacts on the cost of energy generation (CoE) as compare the grid selling price. While doubling the grid buying price, CoE is increased by 14% whereas doubling the grid selling price, CoE is reduced by 2%. This paper also included sensitivity analysis and analyzed that battery cost has highest impacts on CoE followed by PV cost and then grid tariffs. It has been noticed that doubling the cost of battery, PV and grid (one at a time) has increased CoE by 53, 31, and 27%, respectively. The operational energy management strategies presented in this paper, will certainly contribute for PV-battery-based micro-grid developments and evaluating operational performance under electricity energy pricing dynamics.

Activating the potential of decentralized flexibility and energy resources to increase the EV hosting capacity: A case study of a multi-stakeholder local electricity system in Norway

The increasing amount of flexible load in the energy system represents both a challenge and an opportunity. One primary source of load growth is the electrification of the transport sector and the subsequent charging of electric vehicles, which is a load type that can potentially adjust their load profiles. However, to activate the full potential of end-user flexibility, it is necessary to develop pricing mechanisms that can promote efficient load responses on a larger scale. In this paper, a trading mechanism is proposed and analysed within a capacity-based grid tariff scheme by formulating a game-theoretic framework that includes decentralized decision-making by self-interest pursuing end-users. The model is applied to a real-world case in Norway, and it is demonstrated how electrification of vehicles can be achieved with the existing infrastructure. It is found that capacity-based grid tariffs have a limited ability to reduce the coincident peak load in the system since they mainly incentivize individual peak load reductions. However, by including a capacity trading mechanism within the capacity-based tariff structure, we demonstrate that it is possible to increase the value of flexibility since the flexible end-users are incentivized to coordinate their flexibility dispatch with other stakeholders.

Modeling, Analysis and Optimization of Grid-Integrated and Islanded Solar PV Systems for the Ethiopian Residential Sector: Considering an Emerging Utility Tariff Plan for 2021 and Beyond

Currently, difficulties such as the depletion of fossil fuel resources and the associated environmental pollution have driven the rise of other energy systems based on green energy sources. In this research, modeling and a viability study of grid-connected and islanded photovoltaic (PV) power systems for supplying the residential load in Mekelle City, Ethiopia, were carried out considering the country’s emerging utility tariff plan for 2021 and beyond. The technical viability of the proposed supply option was analyzed using PVGIS, PVWatts and HOMER Pro tool, while the economic and environmental optimization aspects were carried out using HOMER Pro. Sensitivity analyses and output comparisons among the three renewable energy simulation tools are presented. The results showed that under the consideration of an incremental electricity tariff plan (up to 2021), the analyzed cost of energy of the grid/PV system is around 12% lower than the utility grid tariff. Moreover, we also found that by taking the continuous global solar PV cost reduction into account, the cost of energy of the modeled islanded operation of solar PV power units totally broke the grid tariff in Ethiopia after 2029 based on the tariff for 2021 and well before with the expected escalation of the grid tariff on an annual basis. The technical performance of the system realized through PVGIS and PVWatts was almost comparable to the HOMER Pro outputs. Thus, this investigation will offer a clear direction to the concerned target groups and policy developers in the evolution of PV power supply options throughout the technically viable locations in the country.

Improved Q-learning for Energy Management in a Grid-tied PV Microgrid

This paper proposes an improved Q-learning method to obtain near-optimal schedules for grid and battery power in a grid-connected electric vehicle charging station for a 24-hour horizon. The charging station is supplied by a solar PV generator with a backup from the utility grid. The grid tariff model is dynamic in line with the smart grid paradigm. First, the mathematical formulation of the problem is developed highlighting each of the cost components considered including battery degradation cost and the real-time tariff for grid power purchase cost. The problem is then formulated as a Markov Decision Process (MDP), i.e., defining each of the parts of a reinforcement learning environment for the charging station’s operation. The MDP is solved using the improved Q-learning algorithm proposed in this paper and the results are compared with the conventional Q-learning method. Specifically, the paper proposes to modify the action-space of a Q-learning algorithm so that each state has just the list of actions that meet a power balance constraint. The Q-table updates are done asynchronously, i.e., the agent does not sweep through the entire state-space in each episode. Simulation results show that the improved Q-learning algorithm returns a 14% lower global cost and achieves higher total rewards than the conventional Q-learning method. Furthermore, it is shown that the improved Q-learning method is more stable in terms of the sensitivity to the learning rate than the conventional Q-learning.

Hybrid renewable energy system optimum design and smart dispatch for nearly Zero Energy Ports

Climate change mitigation has become a ports' emergency; they endeavour to improve their energy efficiency and diminish their carbon footprint. The optimisation analysis of a seaport grid-connected hybrid renewable energy system is presented, comparing the impact of two dispatch strategies and three energy storage systems. Seventeen scenarios have been examined regarding the possible combinations of the most mature renewable and energy storage systems according to the Levelised Cost of Energy, environmental footprint, and future employability. Vanadium Redox Flow Battery based Hybrid Renewable Energy System employed offering 10-h port services' autonomy. For the grid-connected net metering and 24-h port autonomy, the optimal Levelised Cost of Energy is 12.9 c€/kWh. The overall optimum was 8.0 c€/kWh, incorporating 10-h port services' autonomy. All the alternative cases proved much lower of the Levelised Cost of Energy could be much lower than the current grid's tariff. Peak shaving strategy is proven to be more efficient for seaports than cycle charging, resulting in improved energy management and lower energy billing costs by setting peak monthly demand limits. The emissions are diminished to zero, as the net grid purchases are zero, incorporating totally green infrastructure. The nearly Zero Energy Port concept proved to be a viable and feasible solution for ports towards sustainability.

Technical, Economic, Social and Regulatory Feasibility Evaluation of Dynamic Distribution Tariff Designs

The increasing number of distributed energy resources in the distribution grids creates the risk of grid congestion and the high cost of grid expansion. The implementation of the dynamic distribution grid tariffs can potentially avoid grid congestion. Meanwhile, the design and implementation of any distribution tariff need to consider and match the regional/national requirements. However, there is no sufficient evaluation method available to review and evaluate the feasibility of the dynamic distribution tariffs. Therefore, this paper introduces a feasibility evaluation method with four dimensions of technical, economic, social, and regulatory to review dynamic distribution tariffs. The literature on dynamic distribution tariffs is collected, and 29 dynamic distribution tariffs are selected and further categorized into five attributes of rationale, cost drivers, dynamics, events, and active demand. The evaluation results show that the time-of-use tariff is the most feasible dynamic distribution tariff, and the review of a proposed future distribution tariff model in Denmark verifies the evaluation method and results. The developed feasibility evaluation method for dynamic distribution tariffs can ensure the design and implementation of a dynamic distribution tariff to be feasible and applicable in a region.

Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

Incentivising flexible power-to-heat operation in district heating by redesigning electricity grid tariffs

The Danish district heating sector constitutes a large potential for power-to-heat technology utilisation and thereby for increasing energy system flexibility and integration of the heat- and electricity sectors. Though the potential is there, it is uncertain whether the current flat-rate electricity grid tariff structure best incentivises a flexible integration. This study investigates how a redesign of the current flat-rate electricity grid tariffs influences the business-economic incentive for flexible power-to-heat operation in a district heating area, and how tariff schemes can incentivise increased integration of local wind power. The simulation tool energyPRO is used to investigate the influence of three redesigned tariff schemes; a flat-rate tariff reduction, a fixed time-of-use tariff scheme and a dynamic tariff scheme. It is concluded that the redesigned tariff schemes show potential for improving business-economic viability of flexible power-to-heat operation and increased integration of variable renewable electricity. However, measures and careful planning must be undertaken in the design of future tariff schemes to ensure that the necessary income for grid operators remains in place. The study thus suggests a redesign of the current tariff scheme and provides policymakers with tangible results of how a district heating company is affected by changes to the structure of electricity grid tariffs.

Modeling Energy Communities with Collective Photovoltaic Self-Consumption: Synergies between a Small City and a Winery in Portugal

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

Performance and economic viability of the PV system in different climatic zones of Nigeria

This study analyzed the effect of climatic variation on PV systems' performance and economic viability in four climatic zones of Nigeria (warm desert, warm semi-arid, tropical savanna, and monsoon climate). Performance ratio was found to be high in monsoon and tropical savanna climate due to the influence of cool-moist air from the ocean, low temperature, and increased cloud cover in the region. Meanwhile, the least performance ratio was noticed in the warm desert and warm semi-arid climate due to the dusty wind from the Sahara desert, relatively high temperature, and low precipitation in the region. In order to assess the cost-effectiveness and profitability of the solar project in Nigeria, the Levelized cost of electricity (LCOE), net present value (NPV), internal rate of return (IRR), and payback period (PP) were employed. The average LCOE across the climatic zones is 0.21 $/kWh, which is lower compared to the 0.25 $/kWh grid tariff. The average NPV and IRR are found to be $31,164 and 22%, respectively, making the project economically feasible. However, the payback period was found to be ranging from 3.7 to 5.2 years. Finally, the analysis has proven that solar PV technology is economically viable and profitable in Nigeria.

Optimal dispatch of electricity storage supporting the direct supply of industrial consumers by wind generation

A rising number of industrial companies are aiming to increase the share of renewable generation in their electricity consumption, in order to decrease electricity costs or to fulfill portfolio requirements. This can be achieved by using pv or wind generation systems, which are directly connected to the industrial consumer. The volatility of the renewable generation can be balanced by employing an electricity storage system. The paper presents an MILP optimization model for the direct supply of an industrial consumer by a wind farm, coupled with a storage system. The model optimizes the dispatch of the storage system in order to minimize both the energy component as well as the capacity component of the consumers electricity cost. A case study is presented using the grid tariffs in Germany for two different legal scenarios and two load profiles. The operation of a modular compressed air energy storage system is analyzed and the optimal dimensions of the storage power and capacity are determined, for the proposed system.

Optimal Energy Management of a Double-Tracking Grid-Connected Photovoltaic with Battery System for a Microbrewery

Currently, the production of craft beer in microbreweries has become very popular. This craft beer production process is energy intensive due heating and cooling equipment involved. Most of these breweries are classified as commercial loads which are subjected to the applicable commercial time-of-use tariff from the utility company. Renewable energy sources can also be used to assist the breweries in reducing their reliance on the energy from the grid. However, these systems show a common disadvantage of not always meeting the energy demand during the periods where the resources are not available. In this paper, an optimal energy management model of a 5 kWp grid-connected photovoltaic, using a double tracking system with battery storage, is proposed to reduce the microbrewery’s reliance on the grid. For this purpose, a mathematical model describing the system’s variables, objective function, and constraints are developed. Thereafter, the performance of the developed model is simulated and analyzed using the dynamic load demand of a microbrewery brewery as a case study in the South African context. For the selected brewery, the simulation results have shown that for the solar resources as well as applicable grid tariff; up to 53.5% daily operation cost reduction can be achieved when using the proposed system as comped to the usage of the grid alone.