Peak Power Costs Research Articles

Energy management in residential microgrid using model predictive control-based reinforcement learning and Shapley value

This paper presents an Energy Management (EM) strategy for residential microgrid systems using Model Predictive Control (MPC)-based Reinforcement Learning (RL) and Shapley value. We construct a typical residential microgrid system that considers fluctuating spot-market prices, highly uncertain user demand and renewable generation, and collective peak power penalties. To optimize the benefits for all residential prosumers, the EM problem is formulated as a Cooperative Coalition Game (CCG). The objective is to first find an energy trading policy that reduces the collective economic cost (including spot-market cost and peak-power cost) of the residential coalition, and then to distribute the profits obtained through cooperation to all residents. An MPC-based RL approach, which compensates for the shortcomings of MPC and RL and benefits from the advantages of both, is proposed to reduce the monthly collective cost despite the system uncertainties. To determine the amount of monthly electricity bill each resident should pay, we transfer the cost distribution problem into a profit distribution problem. Then, the Shapley value approach is applied to equitably distribute the profits (i.e., cost savings) gained through cooperation to all residents based on the weighted average of their respective marginal contributions. Finally, simulations are performed on a three-household microgrid system located in Oslo, Norway, to validate the proposed strategy, where a real-world dataset of April 2020 is used. Simulation results show that the proposed MPC-based RL approach could effectively reduce the long-term economic cost by about 17.5%, and the Shapley value method provides a solution for allocating the collective bills fairly.

Scenario-Based Stochastic Optimization for Energy and Flexibility Dispatch of a Microgrid

Energy storage is one of the most important components of microgrids with non-dispatchable generators and can offer both energy and flexibility services when the microgrid operates in grid-connected mode. This paper proposes a scenario-based stochastic optimization model that can be used to determine the energy and flexibility dispatch of a residential microgrid with solar and stationary battery systems. The objective of the model is to minimize the expected energy and peak power cost as well as the battery aging cost, while maximizing the expected revenue from flexibility. The formulated stochastic optimization problem is solved in rolling horizon with the uncertainty model being dynamically updated to consider the most recent forecast profiles for solar power and electricity demand. The benefits of the proposed approach were demonstrated by simulating the daily operation of a real building. The results showed that the estimated flexibility was successfully dispatched yielding an economic value of at least 7% of the operation cost of the building microgrid. The model can be used by flexibility providers to assess their flexibility and design a bidding strategy as well as by system operators to design incentives for flexibility providers.

Optimal Location and Sizing of BESS for Performance Improvement of Distribution Systems with High DG Penetration

This work proposes an optimal location and sizing of battery energy storage system (BESS) installation for performance improvement of distribution systems with high distributed generation (DG) penetration level where the DGs comprise photovoltaics (PV) and wind turbines (WT). The installation of the BESS can reduce costs incurred in the systems, alleviate reverse power flow when the systems are in the high DG penetration level, and also achieve peak shaving during high demand. To find the optimal location and sizing of the BESS, three optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), and salp swarm algorithm (SSA), are applied, and their performances are compared. The considered objective function is the system costs consisting of transmission loss cost, peak power cost, and voltage deviation cost. The system performance improvement is compared in terms of transmission loss, peak demand, and voltage regulation reductions. IEEE 33- and 69-bus distribution systems with high DG penetration are tested to investigate the performance improvement of the BESS installation. The results found that the installation of the BESS could successfully decrease system cost, improve voltage profile, reduce power losses, alleviate reverse power flow, and achieve peak shaving where PSO and SSA are found to be the best competitive algorithms. So, the proposed method can be further applied to find the optimal location and sizing of the BESS to improve the performance of practical systems in the future.

Experimental Analysis of Bi-Directional Heat Trading Operation Integrated with Heat Prosumers in Thermal Networks

District cooling and heating methods that can utilize highly efficient heat pumps and various unused new and renewable types of energy are required to achieve low carbon emissions and zero energy usage in buildings and community units. The technical requirements for the implementation of decentralized thermal networks and heat trading are increasing, both for thermal networks in new buildings and for those remodeled based on existing centralized thermal networks. In this study, a conventional centralized thermal network was implemented as a decentralized thermal network and the possibility of heat prosumers feeding thermal networks was demonstrated experimentally. A real-scale plant was constructed by employing unused thermal energy facilities as prosumers in a school and childcare center based on the existing small-scale block heating and cooling thermal network. The decentralized thermal network and heat prosumer concepts were proven through operation experiments performed on the constructed system in summer and winter. An economic benefit can be achieved by increasing the peak power cost. The experimental results also showed that the proposed bi-directional heat trading reduced carbon emissions by 12.7% compared with conventional centralized thermal systems.

Peer-to-peer electricity trading in an industrial site: Value of buildings flexibility on peak load reduction

Local electricity markets and peer-to-peer (P2P) trading schemes in buildings have recently gained importance as an efficient way to incentivize energy flexibility (e.g. consumer demand response or storage) and to share local energy resources (e.g. solar PV). This paper proposes local electricity markets for a complex of industrial buildings. We study P2P electricity trading and analyze the role of sharing local flexibility, e.g. a large battery, to maximise the use of distributed energy resource (DER) technologies. The objective is to investigate the value of P2P electricity trading in combination with on-site flexibility resources for a Norwegian industrial site. As the industrial consumers are exposed to a substantial peak power charge for grid usage, the study analyses how a local market affect the peak power demand management. To analyze it, we developed a linear programming model that represents the local power system characteristics of the buildings and simulate one year in operations. Results indicate potential savings on reducing electricity costs in the range of 6.8% to 11.0% based on P2P trading features. The total cost of peak power is reduced up to 25%, making peak shaving the largest contributor to the net cost savings. Moreover, the industrial site consumes more distributed generation locally, with no DER power curtailment and reduced grid feed-in.

An MILP model for evaluating the optimal operation and flexibility potential of end-users

It is expected that end-users from all sectors should participate in providing system flexibility, as variable renewable energy is increasingly integrated into electricity systems. The ability of end-users to shift their electricity profiles has considerable potentials and can serve many purposes, e.g. to curb the peak load or to increase self-consumption. However, evaluation methods designed for flexibility from conventional power plants may be inadequate for flexibility from end-users due to the diverse constraints of underlying processes and limitations related to individual needs. This work presents a comprehensive and modular flexibility model developed from common operational characteristics of flexible processes as an alternativemethod.The model is applied to two examples: First, the operation of a combined heat-and-power plant including input and output storage is optimized with the objective to increase profits from electricity sales on the spot market. Second, a steel rod production line consisting of melting, casting, and milling processes is scheduled so that operating costs, including peak power costs, are minimized. Moreover, potentials of flexibility as external services and related costs are analysed. In this paper, the model is used to calculate the optimal costs (€), time-dependent flexibility potentials (kWh) and costs of flexibility provision (€/kWh). Thus, the adaptability and versatility of the model are demonstrated. As a modelling template, the model can ease the efforts of stakeholders in the characterization and evaluation of various flexible processes, especially those from small and medium-sized users.

Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study

The cost of peak power for end-users subject to a demand charge may be substantial, expecting to increase further with the vast growth of power-demanding devices. In cases where load-shifting is not a viable option for cost reduction, battery storage systems used for peak shaving purposes are emerging as a promising solution. In this paper, the economic benefits of implementing battery storage into an existing grid-connected photovoltaic system for a medium-scale swimming facility is studied. The objective is to minimize the total cost of electricity for the facility, including the cost of energy and peak power demand, while ensuring the longevity of the battery. An optimization model based on multi-integer linear programming is built, and simulated using a one-year time horizon in GAMS and Matlab. The main results reveal that installing a battery storage system is economically attractive today, with net savings on the total system cost of 0.64% yearly. The cost of peak power is reduced by 13.9%, and the savings from peak shaving operation alone is enough to compensate for the yearly cost of the battery. Moreover, the battery ensures additional revenue by performing price arbitrage operations. When simulating the system for an assumed 2030 scenario, the battery is found to be more profitable with a yearly net savings of 4.15%.

A realistic expectation of electricity production from current design concentrated solar power solar tower with thermal energy storage

AbstractThe costs and electricity production of concentrated solar power (CSP) solar tower (ST) with molten salt (MS) thermal energy storage (TES) technology are here analyzed for the latest, recently dismissed, Aurora project. For a 135 MW net nominal capacity, the likely electricity production is less than the planned 495 GWh/year, corresponding to a capacity factor (CF) of 42%. The dispatchability of this electricity production is also less than the expected. The costs of the electricity produced by this technology are larger than the AU$ 78/MWh of the power purchase agreement (PPA). The larger costs are due to the large‐scale generation certificates (LGCs), valued at around AU$80/MWh, the low‐interest loan of 110 m$AU, and the indirect costs of the larger share of intermittent and unreliable electricity. In a state with peak power prices already up to AU$14000/MWh, the peak power costs are indeed expected to rise further, if the PPA does not specify any minimum amount of electricity to be produced. Given the electricity production of the similar plant of Crescent Dunes, one‐third of the planned, despite more than double the cost, it has been impossible to secure the investments needed even at a cost of AU$ 158/MWh.

Managing Load Uncertainty Leveraging Real-time Scheduling of Energy Storage System

Abstract In this paper, we propose a real time control method for energy storage system (ESS) to minimize the total cost including the loss caused by load prediction error. To minimize total cost, we consider three types of costs: the energy cost from time-of-use (TOU) tariff, the monthly base cost determined from peak power and battery degradation cost. In order to consider the state-dependent battery degradation cost, we use dynamic programming for the optimal policy. Meanwhile, under the Korea commercial and industrial tariff, minimizing the peak power consumption is critical because it determines the monthly base cost and furthermore may affect for the next 12 months. However, peak shaving is vulnerable to the errors from day-ahead load prediction, and thus conservative strategies are desired. To alleviate the performance loss incurred from the load prediction error, we propose a concept of marginal power and learn the relationship between the marginal power and the load prediction errors based on the historical data. By exploiting the marginal power, the real-time ESS charging/discharging power becomes close to the optimal offline policy. Our extensive simulation for one year shows that the proposed method reduces the peak power cost by 45%.

Optimal microgrid scheduling with peak load reduction involving an electrolyzer and flexible loads

This work consists of a multi-objective mixed-integer linear programming model for defining optimized schedules of components in a grid-connected microgrid. The microgrid includes a hydrogen energy system consisting of an alkaline electrolyzer, hydrogen cylinder bundles and a fuel cell for energy storage. Local generation is provided from photovoltaic panels, and the load is given by a fixed load profile combined with a flexible electrical load, which is a battery electric vehicle. The electrolyzer has ramp-up constraints which are modeled explicitly. The objective function includes, besides operational costs and an environmental indicator, a representation of peak power costs, thus leading to an overall peak load reduction under optimized operation. The model is used both for controlling a microgrid in a field trial set-up deployed in South-West Germany and for simulating the microgrid operation for defined period, thus allowing for economic system evaluation. Results from defined sample runs show that the energy storage is primarily used for trimming the peak of electricity drawn from the public grid and is not solely operated with excess power. The flexible demand operation also helps keeping the peak at its possible minimum.

Parasol and GreenSwitch

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal. In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.

Parasol and GreenSwitch

Several companies have recently announced plans to build "green" datacenters, i.e. datacenters partially or completely powered by renewable energy. These datacenters will either generate their own renewable energy or draw it directly from an existing nearby plant. Besides reducing carbon footprints, renewable energy can potentially reduce energy costs, reduce peak power costs, or both. However, certain renewable fuels are intermittent, which requires approaches for tackling the energy supply variability. One approach is to use batteries and/or the electrical grid as a backup for the renewable energy. It may also be possible to adapt the workload to match the renewable energy supply. For highest benefits, green datacenter operators must intelligently manage their workloads and the sources of energy at their disposal. In this paper, we first discuss the tradeoffs involved in building green datacenters today and in the future. Second, we present Parasol, a prototype green datacenter that we have built as a research platform. Parasol comprises a small container, a set of solar panels, a battery bank, and a grid-tie. Third, we describe GreenSwitch, our model-based approach for dynamically scheduling the workload and selecting the source of energy to use. Our real experiments with Parasol, GreenSwitch, and MapReduce workloads demonstrate that intelligent workload and energy source management can produce significant cost reductions. Our results also isolate the cost implications of peak power management, storing energy on the grid, and the ability to delay the MapReduce jobs. Finally, our results demonstrate that careful workload and energy source management can minimize the negative impact of electrical grid outages.

Managing distributed ups energy for effective power capping in data centers

Power over-subscription can reduce costs for modern data centers. However, designing the power infrastructure for a lower operating power point than the aggregated peak power of all servers requires dynamic techniques to avoid high peak power costs and, even worse, tripping circuit breakers. This work presents an architecture for distributed per-server UPSs that stores energy during low activity periods and uses this energy during power spikes. This work leverages the distributed nature of the UPS batteries and develops policies that prolong the duration of their usage. The specific approach shaves 19.4% of the peak power for modern servers, at no cost in performance, allowing the installation of 24% more servers within the same power budget. More servers amortize infrastructure costs better and, hence, reduce total cost of ownership per server by 6.3%.

Bacterial foraging solution based fuzzy logic decision for optimal capacitor allocation in radial distribution system

This paper proposes a novel methodology for the optimal location and sizing of shunt capacitors in radial distribution systems. This method is based on a fuzzy decision making which using a new evolutionary method. The capacitor placement optimization problem includes: minimizing the cost of peak power, reducing energy loss and improving voltage profile. The installation node is selected by the fuzzy reasoning supported by the fuzzy set theory in a step by step procedure. Also an evolutionary algorithm known as bacteria foraging algorithm (BFA) is utilized in solving the objective multivariable optimization problem and the optimal node for capacitor placement is determined. The proposed approach is applied to 34-bus distribution system as a test study and the results are compared with previous method. The results show that this method provides more economic solution by reducing power losses, energy loss, total required capacitive compensation and show a good improvement in nodes voltage to be in the requested range. Comparison between the proposed method in this paper and similar methods in other research works shows the effectiveness of the proposed method for solving optimum capacitor planning problem.